positive and negative effects of artificial intelligence essay

Positive and Negative Impact of Artificial Intelligence

Positive And Negative impact of Artificial Intelligence : The world is developing at a very fast pace. In our ever growing world, technology seems to have taken a centre stage in the affairs of man. With globalization connecting people and places, the interdependence between people has increased, and the need for things and commodities has also increased.

In medicine, engineering, astronomy, and indeed all facets of life, there is the desire of man to make new discoveries.  These discoveries lead to inventions. Inventions are thus made to add value to the human life to either solve a problem or to improve on a situation.

The need to further improve on the situation of life, and also to solve more complex problems have led to the invention of Artificial Intelligence based gadgets. Artificial Intelligence is basically the creating or developing of equipment using complex data programming to coat such equipment with the ability to act, reason and perform a task with little or no supervision. That is, computers are created in such a way that they perform functions based on the programme already created in them even without being operated by human beings.

Just like every other creation of man, Artificial Intelligence has its positive and negative effects on man. The positive and negative effects of Artificial Intelligence or Artificial Intelligence based equipment will be discussed below.

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Table of Contents

Positive Impact of Artificial Intelligence

1. Errors are reduced: There is a huge reduction in human errors with the use of artificial intelligence. Artificial intelligence is programmed to carry out tasks with little or no errors. When computers and machines are deployed in the performance of tasks, there is a minimal chance of mistakes occurring in such tasks.

Accuracy is to a great extent achievable with the use of artificial intelligence. This is because such programmes keyed into such Artificial Intelligence based equipment were designed to solve that particular problem.

2. Artificial intelligence Makes Work Faster: Tasks done with the use of artificial intelligence are usually done faster and also finish in record time. Computers and machines which are employed in the performance of tasks make works to be accomplished fastly.

For example, the deployment of artificial intelligence in the construction of houses makes building projects to be actualized in record time and also allows for more buildings to be erected within a short time unlike when manual labor is employed.

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3. Undertaking of Risks: Artificial intelligence are used to carry out risky jobs that are too dangerous for humans to undertake. Some tasks are highly hazardous to human health and it is only computers and machines that can carry out such tasks. For instance, in the case of pipeline leakage which occurs in a pipeline deep under the sea.

Humans can barely survive such situations or conditions, hence, artificial intelligence is employed to deal with such maintenance. A typical example is the Chenobyl nuclear power plant explosion in Ukraine which occurred at a time Artificial Intelligence was not in vogue. This explosion saw to the death of persons who ventured close to get the situation under control at the earliest moment of the explosion.

4. Ready Availability of Artificial Intelligence : Artificial intelligence (computers, machines and robots) is always readily available when a task is needed to be performed, provided you have the gadget. A machine can never be too busy like humans to carry out a task. Where the machine is available, it is always ready for use provided it is in good condition.

Artificial Intelligence can multitask on a job. Also, artificial intelligence works round the clock. Humans work for specified number of hours a day but Artificial Intelligence can work for 24 hours in a day and still continue working.

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5. Artificial Intelligence Does Monotonous Jobs : Where one does an action over and over again, one becomes bored and may loose the zeal to continue with such task. Unlike humans that gets bored carrying out an activity over and over again, artificial intelligence does not. Computers are designed to carry out a task for as many times as possible and will not get bored of doing that same task.

Monotonous activities bore humans but it is not the case with Artificial Intelligence as they are designed to do a task repetitively without breaking down or getting tired of doing same task over and over again.

6. Artificial Intelligence Helps in new Discoveries and Inventions: The advent of artificial intelligence helps us to detect problems and also create solutions to those problems. For example, in health, with the use of Artificial Intelligence based gadgets, a doctor can easily detect diseases at their early stage.

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Negative Impacts of Artificial Intelligence

1. It is Very Expensive to Produce: The cost of production of Artificial Intelligence based gadgets is very high and expensive. Creating gadgets of this nature is complex. It demands a lot of time, money and energy (both mental and psychological) to create. There is thus, a high cost of production of these equipment.  Again, when such are created or produced, it requires a lot of money to maintain them. They are also very expensive to purchase.

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2. It Requires Highly Skilled Personnel to Operate: Operating an artificial intelligence based equipment requires special skills and experience. One must be properly trained in the art of handling these equipment. The cost of training and also that of hiring a trained expert who can operate the equipment is usually very expensive and may not be easily affordable.

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3. Unemployment and Loss of Jobs: One of the major effects that Artificial Intelligence negatively has on humanity is that it  causes  loss of jobs and unemployment. The use of computers and machines to do tasks which ab initio were done by humans have caused loss of jobs and employment opportunities for people. Workers are being laid off as employers prefer using computers and machines to do the jobs which they (workers) were employed to do. For example, the use of machines in agriculture and building constructions have to a great extent reduced the requirement for manual labour in those areas.

4. It Causes Addiction and Laziness in Humans: Continuous use of artificial intelligence based equipment can be addictive to humans. Again, it causes laziness in man as one no longer does anything by himself but always depends on the use of computers and machines to perform every little task. For example, walking is a form of exercise but rarely do people walk even the shortest distance as a result of cars.

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5. Artificial Intelligence Could be Used to Produce Dangerous Weapons: Humans of evil intents, terrorists and criminals can deploy Artificial Intelligence to make weapons that are dangerous to the well being of humanity. When these AI-based equipment falls into the wrong hands, nuclear weapons as well as other war gadgets can be made by them for the sole aim of using them as implements of war. Again, artificial intelligence based gadgets might turn out to be a Frankenstein creature which ends up destroying its creator.

Artificial Intelligence is a veritable tool which cannot be done away with in modern time. It has helped humanity in many ways as solutions to problems have been proferred and found, coupled with inventions which have made living easier and comfortable. Not minding the negative impacts Artificial Intelligence has on humanity, it still makes the world a better place to live in.

Edeh Samuel Chukwuemeka, ACMC, is a lawyer and a certified mediator/conciliator in Nigeria. He is also a developer with knowledge in various programming languages. Samuel is determined to leverage his skills in technology, SEO, and legal practice to revolutionize the legal profession worldwide by creating web and mobile applications that simplify legal research. Sam is also passionate about educating and providing valuable information to people.

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Experience Stern | Faculty & Research

The ai effect: how artificial intelligence is shaping the economy.

Overview : In the essay “ The Business Revolution: Economy-Wide Impacts of Artificial Intelligence and Digital Platforms ,” NYU Stern Professor Hanna Halaburda , with co-authors Jeffrey Prince (Indiana University), D. Daniel Sokol (USC Marshall), and Feng Zhu (Harvard University), explore themes around digital business transformation and review the impact of artificial intelligence (AI) and digital platforms on specific aspects of the economy: pricing, healthcare, and content.

Why study this now : During the past three decades, business has been transformed from its leveraging of technological advances. The application of this technology has shifted organization value from tangible goods to intangible assets, and from production happening within a company to a dynamic where third parties are instead creating much of the value. The authors of this essay call attention to the overarching effects of AI on business as well as highlight more specific impacts of AI and digital platforms within certain industries.

What the authors spotlight : While AI has the potential to change the way companies work (e.g., increase labor productivity, improve decision-making), its potential for harm should not be overlooked. The authors also shine light on some of the positive and negative effects of AI and digital platforms on certain elements of the economy:

• Pricing : AI pricing algorithms can increase the ability for dynamic pricing, but also may learn to collude over time (even if not explicitly programmed to).
• Healthcare : AI may improve health outcomes for certain diagnoses (e.g., cancer detection, heart disease), but trust in the technology is a major consideration from both the patient and staff sides in terms of adoption.
• Content : Digital technologies have disrupted aspects of industries like music, movies, and books (e.g., reduced costs of distribution, increased differentiation), but challenges like piracy and antitrust concerns exist. 

Furthermore, the authors discuss the potential for mergers and acquisitions to help companies navigate the numerous changes and challenges that digital technology brings.

What does this  change : The authors note that we are in the early stages of the digital business revolution, and that there is growing interest in using empirical research to study digital platforms. They also see an increase in transformations from B2B industries.

Key insight : “The digital business revolution has made existing routines more efficient,” say the authors, “and it has created opportunities to rethink how firms and the economy are organized.”

Table of Contents

What is artificial intelligence, advantages and disadvantages of artificial intelligence, 10 benefits of artificial intelligence, disadvantages of artificial intelligence, advantages and disadvantages of ai in different sectors and industries, choose the right program, advantages and disadvantages of artificial intelligence - the bottom line, advantages and disadvantages of artificial intelligence [ai].

Reviewed and fact-checked by Sayantoni Das

With all the hype around Artificial Intelligence - robots, self-driving cars , etc. - it can be easy to assume that AI doesn’t impact our everyday lives. In reality, most of us encounter Artificial Intelligence in some way or the other almost every single day. From the moment you wake up to check your smartphone to watching another Netflix recommended movie, AI has quickly made its way into our everyday lives. According to a study by Statista, the global AI market is set to grow up to 54 percent every single year . But what exactly is AI? Will it really serve good to mankind in the future? Well, there are tons of advantages and disadvantages of Artificial Intelligence which we’ll discuss in this article. But before we jump into the pros and cons of AI, let us take a quick glance over what is AI.

Before we jump on to the advantages and disadvantages of Artificial Intelligence, let us understand what is AI in the first place. From a birds eye view, AI provides a computer program the ability to think and learn on its own. It is a simulation of human intelligence (hence, artificial) into machines to do things that we would normally rely on humans. This technological marvel extends beyond mere automation, incorporating a broad spectrum of AI skills - abilities that enable machines to understand, reason, learn, and interact in a human-like manner. There are three main types of AI based on its capabilities - weak AI, strong AI, and super AI.

• Weak AI - Focuses on one task and cannot perform beyond its limitations (common in our daily lives)
• Strong AI - Can understand and learn any intellectual task that a human being can (researchers are striving to reach strong AI)
• Super AI - Surpasses human intelligence and can perform any task better than a human (still a concept)

Here's a quick video to help you understand what artificial intelligence is and understand its advantages and disadvantages. 

An artificial intelligence program is a program that is capable of learning and thinking. It is possible to consider anything to be artificial intelligence if it consists of a program performing a task that we would normally assume a human would perform.

While artificial intelligence has many benefits, there are also drawbacks. The benefits of AI include efficiency through task automation, data analysis for informed decisions, assistance in medical diagnosis, and the advancement of autonomous vehicles. The drawbacks of AI include job displacement, ethical concerns about bias and privacy, security risks from hacking, a lack of human-like creativity and empathy.

Let's begin with the advantages of artificial intelligence.

1. Reduction in Human Error

One of the biggest benefits of Artificial Intelligence is that it can significantly reduce errors and increase accuracy and precision. The decisions taken by AI in every step is decided by information previously gathered and a certain set of algorithms . When programmed properly, these errors can be reduced to null. 

An example of the reduction in human error through AI is the use of robotic surgery systems, which can perform complex procedures with precision and accuracy, reducing the risk of human error and improving patient safety in healthcare.

2. Zero Risks

Another big benefit of AI is that humans can overcome many risks by letting AI robots do them for us. Whether it be defusing a bomb, going to space, exploring the deepest parts of oceans, machines with metal bodies are resistant in nature and can survive unfriendly atmospheres. Moreover, they can provide accurate work with greater responsibility and not wear out easily.

One example of zero risks is a fully automated production line in a manufacturing facility. Robots perform all tasks, eliminating the risk of human error and injury in hazardous environments.

3. 24x7 Availability

There are many studies that show humans are productive only about 3 to 4 hours in a day. Humans also need breaks and time offs to balance their work life and personal life. But AI can work endlessly without breaks. They think much faster than humans and perform multiple tasks at a time with accurate results. They can even handle tedious repetitive jobs easily with the help of AI algorithms. 

An example of this is online customer support chatbots, which can provide instant assistance to customers anytime, anywhere. Using AI and natural language processing, chatbots can answer common questions, resolve issues, and escalate complex problems to human agents, ensuring seamless customer service around the clock.

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4. Digital Assistance

Some of the most technologically advanced companies engage with users using digital assistants, which eliminates the need for human personnel. Many websites utilize digital assistants to deliver user-requested content. We can discuss our search with them in conversation. Some chatbots are built in a way that makes it difficult to tell whether we are conversing with a human or a chatbot.

We all know that businesses have a customer service crew that must address the doubts and concerns of the patrons. Businesses can create a chatbot or voice bot that can answer all of their clients' questions using AI.

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5. New Inventions

In practically every field, AI is the driving force behind numerous innovations that will aid humans in resolving the majority of challenging issues.

For instance, recent advances in AI-based technologies  have allowed doctors to detect breast cancer in a woman at an earlier stage.

Another example of new inventions is self-driving cars, which use a combination of cameras, sensors, and AI algorithms to navigate roads and traffic without human intervention. Self-driving cars have the potential to improve road safety, reduce traffic congestion, and increase accessibility for people with disabilities or limited mobility. They are being developed by various companies, including Tesla, Google, and Uber, and are expected to revolutionize transportation.

6. Unbiased Decisions

Human beings are driven by emotions, whether we like it or not. AI on the other hand, is devoid of emotions and highly practical and rational in its approach. A huge advantage of Artificial Intelligence is that it doesn't have any biased views, which ensures more accurate decision-making.

An example of this is AI-powered recruitment systems that screen job applicants based on skills and qualifications rather than demographics. This helps eliminate bias in the hiring process, leading to an inclusive and more diverse workforce.

7. Perform Repetitive Jobs

We will be doing a lot of repetitive tasks as part of our daily work, such as checking documents for flaws and mailing thank-you notes, among other things. We may use artificial intelligence to efficiently automate these menial chores and even eliminate "boring" tasks for people, allowing them to focus on being more creative.

An example of this is using robots in manufacturing assembly lines, which can handle repetitive tasks such as welding, painting, and packaging with high accuracy and speed, reducing costs and improving efficiency.

8. Daily Applications

Today, our everyday lives are entirely dependent on mobile devices and the internet. We utilize a variety of apps, including Google Maps, Alexa, Siri, Cortana on Windows, OK Google, taking selfies, making calls, responding to emails, etc. With the use of various AI-based techniques, we can also anticipate today’s weather and the days ahead.

About 20 years ago, you must have asked someone who had already been there for instructions when you were planning a trip. All you need to do now is ask Google where Bangalore is. The best route between you and Bangalore will be displayed, along with Bangalore's location, on a Google map.

9. AI in Risky Situations

One of the main benefits of artificial intelligence is this. By creating an AI robot that can perform perilous tasks on our behalf, we can get beyond many of the dangerous restrictions that humans face. It can be utilized effectively in any type of natural or man-made calamity, whether it be going to Mars, defusing a bomb, exploring the deepest regions of the oceans, or mining for coal and oil.

For instance, the explosion at the Chernobyl nuclear power facility in Ukraine. As any person who came close to the core would have perished in a matter of minutes, at the time, there were no AI-powered robots that could assist us in reducing the effects of radiation by controlling the fire in its early phases.

10. Medical Applications

AI has also made significant contributions to the field of medicine, with applications ranging from diagnosis and treatment to drug discovery and clinical trials. AI-powered tools can help doctors and researchers analyze patient data, identify potential health risks, and develop personalized treatment plans. This can lead to better health outcomes for patients and help accelerate the development of new medical treatments and technologies.

Let us now look at what are the main disadvantages that Artificial intelligence holds.

1. High Costs

The ability to create a machine that can simulate human intelligence is no small feat. It requires plenty of time and resources and can cost a huge deal of money. AI also needs to operate on the latest hardware and software to stay updated and meet the latest requirements, thus making it quite costly.

2. No Creativity

A big disadvantage of AI is that it cannot learn to think outside the box. AI is capable of learning over time with pre-fed data and past experiences, but cannot be creative in its approach. A classic example is the bot Quill who can write Forbes earning reports . These reports only contain data and facts already provided to the bot. Although it is impressive that a bot can write an article on its own, it lacks the human touch present in other Forbes articles. 

3. Unemployment

One application of artificial intelligence is a robot, which is displacing occupations and increasing unemployment (in a few cases). Therefore, some claim that there is always a chance of unemployment as a result of chatbots and robots replacing humans. 

For instance, robots are frequently utilized to replace human resources in manufacturing businesses in some more technologically advanced nations like Japan. This is not always the case, though, as it creates additional opportunities for humans to work while also replacing humans in order to increase efficiency.

4. Make Humans Lazy

AI applications automate the majority of tedious and repetitive tasks. Since we do not have to memorize things or solve puzzles to get the job done, we tend to use our brains less and less. This addiction to AI can cause problems to future generations.

5. No Ethics

Ethics and morality are important human features that can be difficult to incorporate into an AI. The rapid progress of AI has raised a number of concerns that one day, AI will grow uncontrollably, and eventually wipe out humanity. This moment is referred to as the AI singularity.

6. Emotionless

Since early childhood, we have been taught that neither computers nor other machines have feelings. Humans function as a team, and team management is essential for achieving goals. However, there is no denying that robots are superior to humans when functioning effectively, but it is also true that human connections, which form the basis of teams, cannot be replaced by computers.

7. No Improvement

Humans cannot develop artificial intelligence because it is a technology based on pre-loaded facts and experience. AI is proficient at repeatedly carrying out the same task, but if we want any adjustments or improvements, we must manually alter the codes. AI cannot be accessed and utilized akin to human intelligence, but it can store infinite data.

Machines can only complete tasks they have been developed or programmed for; if they are asked to complete anything else, they frequently fail or provide useless results, which can have significant negative effects. Thus, we are unable to make anything conventional.

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Now that you know both the pros and cons of Artificial Intelligence, one thing is for sure has massive potential for creating a better world to live in. The most important role for humans will be to ensure that the rise of the AI doesn’t get out of hand. Although there are both debatable pros and cons of artificial intelligence , its impact on the global industry is undeniable. It continues to grow every single day driving sustainability for businesses. This certainly calls for the need of AI literacy and upskilling to prosper in many new age jobs. Simplilearn’s Caltech Post Graduate Program in AI & ML will help you fast track your career in AI and prepare you for one of the world’s most exciting jobs. This program covers both AI basics and advanced topics such as deep learning networks , NLP, and reinforcement learning. Get started with this course today and build your dream career in AI.

1. What are the benefits of Artificial Intelligence (AI)?

• Increased Efficiency: AI can automate repetitive tasks, improving efficiency and productivity in various industries.
• Data Analysis and Insights: AI algorithms can analyze large data quickly, providing valuable insights for decision-making.
• 24/7 Availability: AI-powered systems can operate continuously, offering round-the-clock services and support.
• Improved Accuracy: AI can perform tasks with high precision, reducing errors and improving overall accuracy.
• Personalization: AI enables personalized experiences and recommendations based on individual preferences and behavior.
• Safety and Risk Reduction: AI can be used for tasks that are hazardous to humans, reducing risks and ensuring safety.

2. What are the disadvantages of Artificial Intelligence (AI)?

• Job Displacement: AI automation may lead to job losses in certain industries, affecting the job market and workforce.
• Ethical Concerns: AI raises ethical issues, including data privacy, algorithm bias, and potential misuse of AI technologies.
• Lack of Creativity and Empathy: AI lacks human qualities like creativity and empathy, limiting its ability to understand emotions or produce original ideas.
• Cost and Complexity: Developing and implementing AI systems can be expensive, require specialized knowledge and resources.
• Reliability and Trust: AI systems may not always be fully reliable, leading to distrust in their decision-making capabilities.
• Dependency on Technology: Over-reliance on AI can make humans dependent on technology and reduce critical thinking skills.

3. How can businesses benefit from adopting AI? 

Businesses can benefit from adopting AI in various ways, such as:

• Streamlining operations and reducing operational costs.
• Enhancing customer experiences through personalized services and support.
• Optimizing supply chain management and inventory control.
• Predictive analytics for better decision-making and market insights.
• Improve product and service offerings based on customer feedback and data analysis.

4. What are some AI applications in everyday life? 

AI applications in everyday life include:

• Virtual assistants like Siri and Alexa, which help with voice commands and information retrieval.
• Social media algorithms that curate personalized content for users.
• Recommendation systems on streaming platforms, suggesting movies and shows based on viewing history.
•  Financial institutions use fraud detection systems to identify suspicious transactions.
• AI-powered healthcare diagnostics for disease detection and treatment planning.

5. What are the advantages of AI in education?

• Personalized learning: AI has the capability to analyze individual student data, enabling the provision of personalized learning experiences that cater to each student's needs and preferred learning styles. As a result, students can progress at their own pace and receive the necessary assistance for their academic success.
• Improved engagement and motivation: AI can create more interactive and engaging learning experiences that can help students stay focused in their learning.
• Enhanced assessment and feedback: AI can provide more accurate and timely assessment and feedback to help students track their progress and identify areas where they need additional support.
• Increased access to education: AI can help to increase access to education by providing more personalized and affordable learning opportunities.
• Improved teacher training: AI can help to improve teacher training by providing teachers with data and insights that can help them better understand their students and their needs.

6. How does Artificial Intelligence reduce costs?  

AI can reduce costs by automating repetitive tasks, increasing efficiency, and minimizing errors. This leads to improved productivity and resource allocation, ultimately resulting in cost savings.

7. Can AI replace human intelligence and creativity?

 While AI can perform specific tasks with remarkable precision, it cannot fully replicate human intelligence and creativity. AI lacks consciousness and emotions, limiting its ability to understand complex human experiences and produce truly creative works.

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Home — Essay Samples — Information Science and Technology — Artificial Intelligence — Positive And Negative Impacts Of Artificial Intelligence

Positive and Negative Impacts of Artificial Intelligence

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Artificial intelligence is transforming our world — it is on all of us to make sure that it goes well

How ai gets built is currently decided by a small group of technologists. as this technology is transforming our lives, it should be in all of our interest to become informed and engaged..

Why should you care about the development of artificial intelligence?

Think about what the alternative would look like. If you and the wider public do not get informed and engaged, then we leave it to a few entrepreneurs and engineers to decide how this technology will transform our world.

That is the status quo. This small number of people at a few tech firms directly working on artificial intelligence (AI) do understand how extraordinarily powerful this technology is becoming . If the rest of society does not become engaged, then it will be this small elite who decides how this technology will change our lives.

To change this status quo, I want to answer three questions in this article: Why is it hard to take the prospect of a world transformed by AI seriously? How can we imagine such a world? And what is at stake as this technology becomes more powerful?

Why is it hard to take the prospect of a world transformed by artificial intelligence seriously?

In some way, it should be obvious how technology can fundamentally transform the world. We just have to look at how much the world has already changed. If you could invite a family of hunter-gatherers from 20,000 years ago on your next flight, they would be pretty surprised. Technology has changed our world already, so we should expect that it can happen again.

But while we have seen the world transform before, we have seen these transformations play out over the course of generations. What is different now is how very rapid these technological changes have become. In the past, the technologies that our ancestors used in their childhood were still central to their lives in their old age. This has not been the case anymore for recent generations. Instead, it has become common that technologies unimaginable in one's youth become ordinary in later life.

This is the first reason we might not take the prospect seriously: it is easy to underestimate the speed at which technology can change the world.

The second reason why it is difficult to take the possibility of transformative AI – potentially even AI as intelligent as humans – seriously is that it is an idea that we first heard in the cinema. It is not surprising that for many of us, the first reaction to a scenario in which machines have human-like capabilities is the same as if you had asked us to take seriously a future in which vampires, werewolves, or zombies roam the planet. 1

But, it is plausible that it is both the stuff of sci-fi fantasy and the central invention that could arrive in our, or our children’s, lifetimes.

The third reason why it is difficult to take this prospect seriously is by failing to see that powerful AI could lead to very large changes. This is also understandable. It is difficult to form an idea of a future that is very different from our own time. There are two concepts that I find helpful in imagining a very different future with artificial intelligence. Let’s look at both of them.

How to develop an idea of what the future of artificial intelligence might look like?

When thinking about the future of artificial intelligence, I find it helpful to consider two different concepts in particular: human-level AI, and transformative AI. 2 The first concept highlights the AI’s capabilities and anchors them to a familiar benchmark, while transformative AI emphasizes the impact that this technology would have on the world.

From where we are today, much of this may sound like science fiction. It is therefore worth keeping in mind that the majority of surveyed AI experts believe there is a real chance that human-level artificial intelligence will be developed within the next decades, and some believe that it will exist much sooner.

The advantages and disadvantages of comparing machine and human intelligence

One way to think about human-level artificial intelligence is to contrast it with the current state of AI technology. While today’s AI systems often have capabilities similar to a particular, limited part of the human mind, a human-level AI would be a machine that is capable of carrying out the same range of intellectual tasks that we humans are capable of. 3 It is a machine that would be “able to learn to do anything that a human can do,” as Norvig and Russell put it in their textbook on AI. 4

Taken together, the range of abilities that characterize intelligence gives humans the ability to solve problems and achieve a wide variety of goals. A human-level AI would therefore be a system that could solve all those problems that we humans can solve, and do the tasks that humans do today. Such a machine, or collective of machines, would be able to do the work of a translator, an accountant, an illustrator, a teacher, a therapist, a truck driver, or the work of a trader on the world’s financial markets. Like us, it would also be able to do research and science, and to develop new technologies based on that.

The concept of human-level AI has some clear advantages. Using the familiarity of our own intelligence as a reference provides us with some clear guidance on how to imagine the capabilities of this technology.

However, it also has clear disadvantages. Anchoring the imagination of future AI systems to the familiar reality of human intelligence carries the risk that it obscures the very real differences between them.

Some of these differences are obvious. For example, AI systems will have the immense memory of computer systems, against which our own capacity to store information pales. Another obvious difference is the speed at which a machine can absorb and process information. But information storage and processing speed are not the only differences. The domains in which machines already outperform humans is steadily increasing: in chess, after matching the level of the best human players in the late 90s, AI systems reached superhuman levels more than a decade ago. In other games like Go or complex strategy games, this has happened more recently. 5

These differences mean that an AI that is at least as good as humans in every domain would overall be much more powerful than the human mind. Even the first “human-level AI” would therefore be quite superhuman in many ways. 6

Human intelligence is also a bad metaphor for machine intelligence in other ways. The way we think is often very different from machines, and as a consequence the output of thinking machines can be very alien to us.

Most perplexing and most concerning are the strange and unexpected ways in which machine intelligence can fail. The AI-generated image of the horse below provides an example: on the one hand, AIs can do what no human can do – produce an image of anything, in any style (here photorealistic), in mere seconds – but on the other hand it can fail in ways that no human would fail. 7 No human would make the mistake of drawing a horse with five legs. 8

Imagining a powerful future AI as just another human would therefore likely be a mistake. The differences might be so large that it will be a misnomer to call such systems “human-level.”

AI-generated image of a horse 9

Transformative artificial intelligence is defined by the impact this technology would have on the world

In contrast, the concept of transformative AI is not based on a comparison with human intelligence. This has the advantage of sidestepping the problems that the comparisons with our own mind bring. But it has the disadvantage that it is harder to imagine what such a system would look like and be capable of. It requires more from us. It requires us to imagine a world with intelligent actors that are potentially very different from ourselves.

Transformative AI is not defined by any specific capabilities, but by the real-world impact that the AI would have. To qualify as transformative, researchers think of it as AI that is “powerful enough to bring us into a new, qualitatively different future.” 10

In humanity’s history, there have been two cases of such major transformations, the agricultural and the industrial revolutions.

Transformative AI becoming a reality would be an event on that scale. Like the arrival of agriculture 10,000 years ago, or the transition from hand- to machine-manufacturing, it would be an event that would change the world for billions of people around the globe and for the entire trajectory of humanity’s future .

Technologies that fundamentally change how a wide range of goods or services are produced are called ‘general-purpose technologies’. The two previous transformative events were caused by the discovery of two particularly significant general-purpose technologies: the change in food production as humanity transitioned from hunting and gathering to farming, and the rise of machine manufacturing in the industrial revolution. Based on the evidence and arguments presented in this series on AI development, I believe it is plausible that powerful AI could represent the introduction of a similarly significant general-purpose technology.

Timeline of the three transformative events in world history

A future of human-level or transformative AI?

The two concepts are closely related, but they are not the same. The creation of a human-level AI would certainly have a transformative impact on our world. If the work of most humans could be carried out by an AI, the lives of millions of people would change. 11

The opposite, however, is not true: we might see transformative AI without developing human-level AI. Since the human mind is in many ways a poor metaphor for the intelligence of machines, we might plausibly develop transformative AI before we develop human-level AI. Depending on how this goes, this might mean that we will never see any machine intelligence for which human intelligence is a helpful comparison.

When and if AI systems might reach either of these levels is of course difficult to predict. In my companion article on this question, I give an overview of what researchers in this field currently believe. Many AI experts believe there is a real chance that such systems will be developed within the next decades, and some believe that they will exist much sooner.

What is at stake as artificial intelligence becomes more powerful?

All major technological innovations lead to a range of positive and negative consequences. For AI, the spectrum of possible outcomes – from the most negative to the most positive – is extraordinarily wide.

That the use of AI technology can cause harm is clear, because it is already happening.

AI systems can cause harm when people use them maliciously. For example, when they are used in politically-motivated disinformation campaigns or to enable mass surveillance. 12

But AI systems can also cause unintended harm, when they act differently than intended or fail. For example, in the Netherlands the authorities used an AI system which falsely claimed that an estimated 26,000 parents made fraudulent claims for child care benefits. The false allegations led to hardship for many poor families, and also resulted in the resignation of the Dutch government in 2021. 13

As AI becomes more powerful, the possible negative impacts could become much larger. Many of these risks have rightfully received public attention: more powerful AI could lead to mass labor displacement, or extreme concentrations of power and wealth. In the hands of autocrats, it could empower totalitarianism through its suitability for mass surveillance and control.

The so-called alignment problem of AI is another extreme risk. This is the concern that nobody would be able to control a powerful AI system, even if the AI takes actions that harm us humans, or humanity as a whole. This risk is unfortunately receiving little attention from the wider public, but it is seen as an extremely large risk by many leading AI researchers. 14

How could an AI possibly escape human control and end up harming humans?

The risk is not that an AI becomes self-aware, develops bad intentions, and “chooses” to do this. The risk is that we try to instruct the AI to pursue some specific goal – even a very worthwhile one – and in the pursuit of that goal it ends up harming humans. It is about unintended consequences. The AI does what we told it to do, but not what we wanted it to do.

Can’t we just tell the AI to not do those things? It is definitely possible to build an AI that avoids any particular problem we foresee, but it is hard to foresee all the possible harmful unintended consequences. The alignment problem arises because of “the impossibility of defining true human purposes correctly and completely,” as AI researcher Stuart Russell puts it. 15

Can’t we then just switch off the AI? This might also not be possible. That is because a powerful AI would know two things: it faces a risk that humans could turn it off, and it can’t achieve its goals once it has been turned off. As a consequence, the AI will pursue a very fundamental goal of ensuring that it won’t be switched off. This is why, once we realize that an extremely intelligent AI is causing unintended harm in the pursuit of some specific goal, it might not be possible to turn it off or change what the system does. 16

This risk – that humanity might not be able to stay in control once AI becomes very powerful, and that this might lead to an extreme catastrophe – has been recognized right from the early days of AI research more than 70 years ago. 17 The very rapid development of AI in recent years has made a solution to this problem much more urgent.

I have tried to summarize some of the risks of AI, but a short article is not enough space to address all possible questions. Especially on the very worst risks of AI systems, and what we can do now to reduce them, I recommend reading the book The Alignment Problem by Brian Christian and Benjamin Hilton’s article ‘Preventing an AI-related catastrophe’ .

If we manage to avoid these risks, transformative AI could also lead to very positive consequences. Advances in science and technology were crucial to the many positive developments in humanity’s history. If artificial ingenuity can augment our own, it could help us make progress on the many large problems we face: from cleaner energy, to the replacement of unpleasant work, to much better healthcare.

This extremely large contrast between the possible positives and negatives makes clear that the stakes are unusually high with this technology. Reducing the negative risks and solving the alignment problem could mean the difference between a healthy, flourishing, and wealthy future for humanity – and the destruction of the same.

How can we make sure that the development of AI goes well?

Making sure that the development of artificial intelligence goes well is not just one of the most crucial questions of our time, but likely one of the most crucial questions in human history. This needs public resources – public funding, public attention, and public engagement.

Currently, almost all resources that are dedicated to AI aim to speed up the development of this technology. Efforts that aim to increase the safety of AI systems, on the other hand, do not receive the resources they need. Researcher Toby Ord estimated that in 2020 between $10 to $50 million was spent on work to address the alignment problem. 18 Corporate AI investment in the same year was more than 2000-times larger, it summed up to $153 billion.

This is not only the case for the AI alignment problem. The work on the entire range of negative social consequences from AI is under-resourced compared to the large investments to increase the power and use of AI systems.

It is frustrating and concerning for society as a whole that AI safety work is extremely neglected and that little public funding is dedicated to this crucial field of research. On the other hand, for each individual person this neglect means that they have a good chance to actually make a positive difference, if they dedicate themselves to this problem now. And while the field of AI safety is small, it does provide good resources on what you can do concretely if you want to work on this problem.

I hope that more people dedicate their individual careers to this cause, but it needs more than individual efforts. A technology that is transforming our society needs to be a central interest of all of us. As a society we have to think more about the societal impact of AI, become knowledgeable about the technology, and understand what is at stake.

When our children look back at today, I imagine that they will find it difficult to understand how little attention and resources we dedicated to the development of safe AI. I hope that this changes in the coming years, and that we begin to dedicate more resources to making sure that powerful AI gets developed in a way that benefits us and the next generations.

If we fail to develop this broad-based understanding, then it will remain the small elite that finances and builds this technology that will determine how one of the – or plausibly the – most powerful technology in human history will transform our world.

If we leave the development of artificial intelligence entirely to private companies, then we are also leaving it up these private companies what our future — the future of humanity — will be.

With our work at Our World in Data we want to do our small part to enable a better informed public conversation on AI and the future we want to live in. You can find these resources on OurWorldinData.org/artificial-intelligence

Acknowledgements: I would like to thank my colleagues Daniel Bachler, Charlie Giattino, and Edouard Mathieu for their helpful comments to drafts of this essay.

This problem becomes even larger when we try to imagine how a future with a human-level AI might play out. Any particular scenario will not only involve the idea that this powerful AI exists, but a whole range of additional assumptions about the future context in which this happens. It is therefore hard to communicate a scenario of a world with human-level AI that does not sound contrived, bizarre or even silly.

Both of these concepts are widely used in the scientific literature on artificial intelligence. For example, questions about the timelines for the development of future AI are often framed using these terms. See my article on this topic .

The fact that humans are capable of a range of intellectual tasks means that you arrive at different definitions of intelligence depending on which aspect within that range you focus on (the Wikipedia entry on intelligence , for example, lists a number of definitions from various researchers and different disciplines). As a consequence there are also various definitions of ‘human-level AI’.

There are also several closely related terms: Artificial General Intelligence, High-Level Machine Intelligence, Strong AI, or Full AI are sometimes synonymously used, and sometimes defined in similar, yet different ways. In specific discussions, it is necessary to define this concept more narrowly; for example, in studies on AI timelines researchers offer more precise definitions of what human-level AI refers to in their particular study.

Peter Norvig and Stuart Russell (2021) — Artificial Intelligence: A Modern Approach. Fourth edition. Published by Pearson.

The AI system AlphaGo , and its various successors, won against Go masters. The AI system Pluribus beat humans at no-limit Texas hold 'em poker. The AI system Cicero can strategize and use human language to win the strategy game Diplomacy. See: Meta Fundamental AI Research Diplomacy Team (FAIR), Anton Bakhtin, Noam Brown, Emily Dinan, Gabriele Farina, Colin Flaherty, Daniel Fried, et al. (2022) – ‘Human-Level Play in the Game of Diplomacy by Combining Language Models with Strategic Reasoning’. In Science 0, no. 0 (22 November 2022): eade9097. https://doi.org/10.1126/science.ade9097 .

This also poses a problem when we evaluate how the intelligence of a machine compares with the intelligence of humans. If intelligence was a general ability, a single capacity, then we could easily compare and evaluate it, but the fact that it is a range of skills makes it much more difficult to compare across machine and human intelligence. Tests for AI systems are therefore comprising a wide range of tasks. See for example Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, Jacob Steinhardt (2020) –  Measuring Massive Multitask Language Understanding or the definition of what would qualify as artificial general intelligence in this Metaculus prediction .

An overview of how AI systems can fail can be found in Charles Choi – 7 Revealing Ways AIs Fail . It is also worth reading through the AIAAIC Repository which “details recent incidents and controversies driven by or relating to AI, algorithms, and automation."

I have taken this example from AI researcher François Chollet , who published it here .

Via François Chollet , who published it here . Based on Chollet’s comments it seems that this image was created by the AI system ‘Stable Diffusion’.

This quote is from Holden Karnofsky (2021) – AI Timelines: Where the Arguments, and the "Experts," Stand . For Holden Karnofsky’s earlier thinking on this conceptualization of AI see his 2016 article ‘Some Background on Our Views Regarding Advanced Artificial Intelligence’ .

Ajeya Cotra, whose research on AI timelines I discuss in other articles of this series, attempts to give a quantitative definition of what would qualify as transformative AI. in her widely cited report on AI timelines she defines it as a change in software technology that brings the growth rate of gross world product "to 20%-30% per year". Several other researchers define TAI in similar terms.

Human-level AI is typically defined as a software system that can carry out at least 90% or 99% of all economically relevant tasks that humans carry out. A lower-bar definition would be an AI system that can carry out all those tasks that can currently be done by another human who is working remotely on a computer.

On the use of AI in politically-motivated disinformation campaigns see for example John Villasenor (November 2020) – How to deal with AI-enabled disinformation . More generally on this topic see Brundage and Avin et al. (2018) – The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation, published at maliciousaireport.com . A starting point for literature and reporting on mass surveillance by governments is the relevant Wikipedia entry .

See for example the Wikipedia entry on the ‘Dutch childcare benefits scandal’ and Melissa Heikkilä (2022) – ‘Dutch scandal serves as a warning for Europe over risks of using algorithms’ , in Politico. The technology can also reinforce discrimination in terms of race and gender. See Brian Christian’s book The Alignment Problem and the reports of the AI Now Institute .

Overviews are provided in Stuart Russell (2019) – Human Compatible (especially chapter 5) and Brian Christian’s 2020 book The Alignment Problem . Christian presents the thinking of many leading AI researchers from the earliest days up to now and presents an excellent overview of this problem. It is also seen as a large risk by some of the leading private firms who work towards powerful AI – see OpenAI's article " Our approach to alignment research " from August 2022.

Stuart Russell (2019) – Human Compatible

A question that follows from this is, why build such a powerful AI in the first place?

The incentives are very high. As I emphasize below, this innovation has the potential to lead to very positive developments. In addition to the large social benefits there are also large incentives for those who develop it – the governments that can use it for their goals, the individuals who can use it to become more powerful and wealthy. Additionally, it is of scientific interest and might help us to understand our own mind and intelligence better. And lastly, even if we wanted to stop building powerful AIs, it is likely very hard to actually achieve it. It is very hard to coordinate across the whole world and agree to stop building more advanced AI – countries around the world would have to agree and then find ways to actually implement it.

In 1950 the computer science pioneer Alan Turing put it like this: “If a machine can think, it might think more intelligently than we do, and then where should we be? … [T]his new danger is much closer. If it comes at all it will almost certainly be within the next millennium. It is remote but not astronomically remote, and is certainly something which can give us anxiety. It is customary, in a talk or article on this subject, to offer a grain of comfort, in the form of a statement that some particularly human characteristic could never be imitated by a machine. … I cannot offer any such comfort, for I believe that no such bounds can be set.” Alan. M. Turing (1950) – Computing Machinery and Intelligence , In Mind, Volume LIX, Issue 236, October 1950, Pages 433–460.

Norbert Wiener is another pioneer who saw the alignment problem very early. One way he put it was “If we use, to achieve our purposes, a mechanical agency with whose operation we cannot interfere effectively … we had better be quite sure that the purpose put into the machine is the purpose which we really desire.” quoted from Norbert Wiener (1960) – Some Moral and Technical Consequences of Automation: As machines learn they may develop unforeseen strategies at rates that baffle their programmers. In Science.

In 1950 – the same year in which Turing published the cited article – Wiener published his book The Human Use of Human Beings, whose front-cover blurb reads: “The ‘mechanical brain’ and similar machines can destroy human values or enable us to realize them as never before.”

Toby Ord – The Precipice . He makes this projection in footnote 55 of chapter 2. It is based on the 2017 estimate by Farquhar.

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30815 Artificial Intelligence as a positive and negative factor in global risk

• Published: July 2008
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By far the greatest danger of Artificial Intelligence (AI) is that people conclude too early that they understand it. Of course, this problem is not limited to the field of AI. Jacques Monod wrote: ‘A curious aspect of the theory of evolution is that everybody thinks he understands it’ (Monod, 1974). The problem seems to be unusually acute in Artificial Intelligence. The field of AI has a reputation for making huge promises and then failing to deliver on them. Most observers conclude that AI is hard, as indeed it is. But the embarrassment does not stem from the difficulty. It is difficult to build a star from hydrogen, but the field of stellar astronomy does not have a terrible reputation for promising to build stars and then failing. The critical inference is not that AI is hard, but that, for some reason, it is very easy for people to think they know far more about AI than they actually do. It may be tempting to ignore Artificial Intelligence because, of all the global risks discussed in this book, AI is probably hardest to discuss. We cannot consult actuarial statistics to assign small annual probabilities of catastrophe, as with asteroid strikes. We cannot use calculations from a precise, precisely confirmed model to rule out events or place infinitesimal upper bounds on their probability, as with proposed physics disasters. But this makes AI catastrophes more worrisome, not less. The effect of many cognitive biases has been found to increase with time pressure, cognitive busyness, or sparse information. Which is to say that the more difficult the analytic challenge, the more important it is to avoid or reduce bias. Therefore I strongly recommend reading my other chapter (Chapter 5) in this book before continuing with this chapter. When something is universal enough in our everyday lives, we take it for granted to the point of forgetting it exists. Imagine a complex biological adaptation with ten necessary parts. If each of the ten genes is independently at 50% frequency in the gene pool – each gene possessed by only half the organisms in that species – then, on average, only 1 in 1024 organisms will possess the full, functioning adaptation.

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Artificial Intelligence: Positive or Negative Innovation? Essay

Introduction, artificial intelligence, positive effects of artificial innovation, negative implications of adopting ai.

Undeniably, innovations are good for the prosperity and enhancement of human’s standards of living. With this regard, technology and innovations in general should not be seen as negative. However, there is great need for caution when deciding the innovations that are helpful and those that are harmful and dangerous. This paper seeks to show that there are two effects of innovations which can either be positive or negative.

Artificial Intelligence is one of the most controversial innovations that the world has seen as per the current developments. The other closely related innovation that has also raised similar attention is the human cloning scientific development. With the artificial development, it is believed that the research will take a century before it is completed. According to the head of the Google’s self driving car project, Sebastian Thrun, artificial intelligence is taking over the world (Lemmer & Kanal 2014). He argues that while humans will still be in charge of a few aspects of life in the near future, their control will be reduced due to the development of artificial intelligence.

Artificial Intelligence can be very beneficial to the human race in a number of ways. One of the most significant ways that AI can help humans is by the degree by which it can be used in areas such as healthcare, education, and business among other areas (Matsuda, Cohen & Koedinger 2015). The advancement of AI can greatly enhance the accuracy and effectiveness of medical services hence improving humans’ livelihoods. The recent development of a self driving vehicle shows how our lives can become easier with the help of AI.

AI on the other hand has many unfavorable effects on human livelihood. For instance, the self-driven vehicle may be a good idea and its sufficiency and ability to automatically avoid collision may reduce the rate of accidents. Nonetheless, if we consider human survival, this may not be one of the best things to do. The implications of this innovation affect aspects such as employment. Having self-driven cabs in the streets will send human drivers home and reduce the rate of employment. Automated vendor machines are the best examples to show how AI can rob humanity of its natural setting.

Due to the self-running vendor machines, many energy and snack vendors have closed shopped due to lack of market. Nobody in investing on vending stores anymore since one can easily purchase a vending machine and find a vending space. The same happened with the automated teller machines which led to the laying off of several human tellers in the banking industry. The current robotics are directed and utilized under the supervision of humans and their movements and intentions are decided by people.

Determining whether AI is a friendly innovation or it is a global mistake depends on the functions that the innovation is intended to achieve. The development of AI and its inclusion in the development of war robots and drones will give the gadgets autonomous decision making capabilities (Müller & Bostrom 2014). In this case, such machines are able to reason and decide when to attack and when not to. With this power, humanity may be losing its control over the world and things can turn out to be chaotic. Giving machines the power to reason and decide independently can be such a dangerous move especially in military development and innovations.

Lemmer, F & Kanal, L 2014, ‘Qualitative probabilistic networks for planning under uncertainty’, Uncertainty in Artificial Intelligence, vol. 2, no.1, pp. 197.

Matsuda, N, Cohen, W & Koedinger, K 2015, ‘Teaching the teacher: tutoring SimStudent leads to more effective cognitive tutor authoring’, International Journal of Artificial Intelligence in Education , vol. 25, no.1, pp.1-34.

Müller, V & Bostrom, N 2014, ‘Future progress in artificial intelligence: A poll among experts,’ AI Matters , vol. 1, no.1, pp. 9-11.

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IvyPanda. (2020, September 12). Artificial Intelligence: Positive or Negative Innovation? https://ivypanda.com/essays/artificial-intelligence-positive-or-negative-innovation/

"Artificial Intelligence: Positive or Negative Innovation?" IvyPanda , 12 Sept. 2020, ivypanda.com/essays/artificial-intelligence-positive-or-negative-innovation/.

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IvyPanda . 2020. "Artificial Intelligence: Positive or Negative Innovation?" September 12, 2020. https://ivypanda.com/essays/artificial-intelligence-positive-or-negative-innovation/.

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10 Positive Impacts of Artificial Intelligence

Healthcare, accessibility, and other ways AI is here to help

• Western Kentucky University
• Gulf Coast Community College

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Digital Assistants and Smart Home Devices

Streaming services, creative work, fitness plans, work projects, creating accessibility, combating climate change, winning the war on drugs, and even better healthcare, how artificial intelligence can help the world.

• Frequently Asked Questions

Artificial intelligence suddenly seems like all you hear about, but it didn’t just happen. Scientists have been working toward creating working AI models for years . Only recently have they managed to create something that can be useful.

At first glance, it might seem that AI is really only good for language or image processing: You can have AI write a story or create an image. Some creatives have even used it to create complete books . Or, if you’ve really been following AI developments, you might have seen all the stories about how AI has gone off the rails . 

Despite all the doom and gloom, AI isn’t all bad. Quite the opposite is true. Here are 10 of the best uses for AI in your daily life.

Suppose you own an Amazon Alexa device, an Apple product with Siri, a computer that leverages Cortana, or an Android-based device with Google Assistant. In that case, you may not realize it, but you already use AI regularly. Digital assistants are software applications that can learn your voice, recognize your speech pattern, and even monitor your environment to send an alert at the sound of breaking glass or when other triggers take place. And statistics show that more than 40 percent of people use these digital assistants somehow.

Omid Armin / Unsplash

Digital assistants use Natural Language Processing, which is the ability to recognize words and phrases that are commonly used and to ‘infer’ the response you’re looking for based on those words and phrases. Moreover, they can even be programmed to complete complicated routines like dimming the lights, changing the temperature, and reading specific information aloud when triggered using the right phrase. 

Almost 80 percent of people worldwide listen to music through some streaming service. That’s a lot of music streaming. And AI is getting in on the action there, too. For example, Spotify has an AI-enhanced streaming service in beta testing that will create playlists for you based on the music you’ve listened to and explain why it chose the songs on that playlist.

You may have also used AI if you use Netflix or Amazon Prime. Both services have AI-enhanced capabilities that help create recommendations for what you might want to watch. Netflix even tried its hand at creating an anime film using AI . 

If you work (or play) in a creative environment and use software applications like Adobe After Effects or Premiere Pro, you may have used AI there. The company has added AI capabilities to help creators during the editing process, specifically for video creators.

But digital creators aren’t the only ones who can benefit from AI. Programs like Magic Canvas for the Glowforge laser cutter have incorporated AI to help creators move an idea from conception to creation. Even woodworkers are using AI to design woodworking projects in ways they may not have previously been able to. 

Fitness is another area that has gotten lots of attention over the past few years as more and more people realize the need for a healthier lifestyle. As a result, some people have turned to AI to create fitness plans tailored to their specific needs, styles, and preferred focus.

Apps like FitnessAI are popping up online to help novice users dig even deeper into AI capabilities for a truly customized workout. And that doesn’t even consider all of the already available apps that use AI to nudge people toward healthier lifestyle choices. Nor does it include the software that’s built into wearable devices. 

Researchers are working on ways to integrate AI into education at all levels. For example, researchers from Clemson University are developing AI that can customize lesson plans based on a student’s needs, while researchers at Stanford have created a prototype for an AI-enhanced biology textbook that helps students find the answers they need and engages with those students to deepen their understanding of the subject.

Even beyond basic instruction, AI can give students a boost in language skills. Programs like Grammarly learn from a person’s writing style or targets that person has outlined for a document and makes suggestions to improve their writing. Grammarly can even help teachers by sniffing out plagiarism.  

Recently, a major area of focus has been on creating AI that can help people do common tasks faster. What better place to apply that kind of AI than in the necessary communications people must create and send daily at their jobs? For example, if you’ve ever used Gmail and noticed that it offers suggestions while writing an email—that’s AI.

Microsoft is also getting into the game with Copilot, an AI assistant designed to help you create documents, presentations, and reports. It can even create email responses, monitor calendars to sync up meeting times, and create PivotTables in Excel. 

Probably one of the greatest advantages of AI is its ability to help people with disabilities. For example, a feature of MidJourney, an image-generating AI, will automatically write alt text for images . Alt text describes the image screen readers use to help people with disabilities deciphers the images on a page.

University of Central Florida

Another advancement that AI can facilitate is the diagnosis of disabilities. There was a time when a person might make it into adulthood before they were diagnosed with an attention deficit disorder, dyslexia, or even Asperger’s syndrome. AI can now examine patterns of behavior , test results, and other information to diagnose these and other conditions. What’s more, once the diagnosis is made, AI can help create a plan of care to help the individual learn to function more effectively with the disorder or overcome it completely. 

Climate change is at the forefront of nearly everyone’s mind. Changes in weather patterns and how animals behave, differences in our atmosphere, and even increases in diseases can be attributed to climate change. And AI can help combat those issues.

For example, AI is used to help monitor climate change and create recommendations for reducing emissions. It also has the ability to unearth facts and data that were previously overlooked, which helps researchers to look more creatively at the problems our planet is facing, which in turn helps those scientists to find better solutions to the problems we face. AI could even help us save the bees that are so vital to our ecosystem. 

An unexpected benefit of having AI that can crunch billions of bits of data is that it can make connections faster than humans will ever be able to. Think about drug molecules that could be used to create new, more dangerous designer drugs. AI can connect the dots between those combinations , which scientists and law enforcement agencies can then use to prevent the creation of those drugs.

Taking that whole idea up a notch, AI can also use that information to create safer, more effective drugs to treat diseases that have never been treatable before. 

If AI can be used to create better drugs, it can also improve our healthcare to levels that we’ve only dreamed about. For example, AI can create connections in a patient record that might point to early symptoms of a disease. Or it could be used to identify disease markers in areas difficult to differentiate.

However AI is used in healthcare, its ability to use vast amounts of data to make connections that have never been made before opens a world of possibilities for the cures and preventative measures that may be coming in the near future.

Taken together, it’s easy to see the possibilities for artificial intelligence to positively impact our world. And we haven’t even gotten to chatbots, banking and finance, social media, or facial recognition.

Are there still many nuances that need to be considered, addressed, and maybe even legislated? Absolutely. But we’ve only scratched the surface of the technology, and there are so many possibilities. It’s exciting to see what might come next.

AGI stands for "artificial general intelligence." It's a theoretical version of AI that can do anything a human brain can do with minimal input or guidance. Another term for it is "strong AI," which differs from currently available algorithms (known as "weak AI").

One commonplace example of an AI is the digital assistant on your smartphone or computer (e.g., Siri, Alexa, Cortana, and Bixby). Other AI systems you may see around are social-media algorithms and text generators like ChatGPT.

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How Is AI Changing the Way We Write and Create?

Since late last year, artificial intelligence platforms like ChatGPT have become a growing topic of conversation on college campuses, with students using the technology for everything from class assignments to essays.

These text-generating software programs sift through massive databases to generate human-like responses to prompts or questions from users. The rapid introduction of this technology and its relatively unknown potential have spawned both awe and apprehension. 

So what are the possible positive and negative effects of these tools? How might they change how we think about writing, creativity, authenticity and teaching? What is the path forward?

To better understand these questions, we spoke with three scholars from our Department of English . Our faculty offer insights on the technology’s societal and ethical ramifications, how to deal with issues like plagiarism, and the importance of increasing AI literacy, among other topics.

What are the cognitive consequences of machine-generated writing?  

Chris Anson Professor of English

Written communication represents myriad purposes and genres across thousands of different contexts. Much of it is created to handle routine tasks, such as describing a home for a real estate listing, sending an apology letter to a customer or providing accurate directions to a location.

In such cases, using AI-based natural language processors (NLP) to fulfill the task does little to challenge the cognitive processes of the human writer. The reason is that the writer is ordinarily not significantly changed by the writing task, especially when it uses boilerplate-like language — language that is often repeated with a few unique details inserted. Instead, these softwares improve efficiency and make time for the person to do higher-level, more cognitively sophisticated kinds of writing. 

However, when people compose unique texts that require complex reasoning — the framing and support of arguments, and choices of structure, language and style — their composing process alternates between mental formulation and textual output. Writers test and evaluate their visual representation of thought on a page or screen as it emerges, discovering new ideas and subsequently revising the text. Writing can change the writer, opening up new perspectives and beliefs or revealing what there still is to learn.

Especially in educational settings, the reciprocity of writing and thinking is essential for intellectual development and higher-order reasoning. Asking an AI-based system to write an essay on a topic that the (human) writer has not yet explored significantly subverts the thinking and learning process. 

It is possible for a writer to auto-generate a text, evaluate whether it reflects the writer’s thoughts or intentions, and then revise the text as needed. But this process doesn’t work for many genres such as explanations of scientific processes or historical accounts because the writer is relying on the machine to provide information they don’t yet know. 

Representing new knowledge in writing solidifies the knowledge through its (re)articulation, leading to stronger learning. In this sense, when used to replace human composing entirely, AI-based NLP systems threaten the integrity of our educational system and the future intellectual acumen of our students.

When used to replace human composing entirely, AI-based NLP systems threaten the integrity of our educational system and the future intellectual acumen of our students.

Because NLP systems increasingly will become part of our daily lives, educators need to find principled ways to integrate them into instruction. For example, I asked ChatGPT to explain the cognitive consequences of machine-generated writing, it gave me an additional idea that I had not considered, but only after I had written my statement. The software’s response? “Machine-generated writing may lead to a homogenization of writing style…”

As ChatGPT itself recommends, “it is important for people to remain aware of these potential effects and to use machine-generated writing in conjunction with, rather than as a replacement for, human-generated writing.” 

  What are some ethical considerations of machine-generated content?

Huiling Ding Professor of English

AI-generated content, be it texts or artwork, introduces many ethical challenges related to authorship, copyright, creativity, plagiarism and labor practices. For instance, text-to-image AI generators like Midjourney and DALL-E 2 use images available in the public domain and/or images available online through Google search, Pinterest, and other image-sharing and art-shopping platforms as training data for their algorithms.

By supporting text-prompt-driven image creation, these AI generators then produce artwork that can imitate individual artists’ styles. In doing so, they compete with if not displace artists who have spent decades improving their craft. 

AI-assisted writing faces similar challenges in terms of transparency, explainability, plagiarism and authorship attribution. Using online texts as training data, AI writers such as GPT-3 can generate original summaries and syntheses based on existing content. 

Traditional writing classes are disrupted by these AI tools, which speed up and automate the process of online research and the summary and synthesis of reference materials. Students can easily copy and paste AI-generated content as their own written work without being caught by plagiarism-detecting tools such as Turnitin.

In other words, natural language generation tools such as GPT-3 transform how we detect and define plagiarism. That, in turn, calls for new research and adaptation from writing instructors and scholars. 

Outside the classroom, professional writers and businesses use AI content generators to create preliminary ideas, generate quick summaries of online publications, write stories and engage with customers in chatbot conversations.

While famous artists such as Greg Rutkowski may feel their rights infringed by AI art generators, other artists are using AI-generated art for inspiration. In the content generation marketplace, these AI tools can compete with writers and artists or can be used as human-augmenting tools to help writers and artists produce content more creatively, efficiently and collaboratively. 

How do advancements in AI technologies affect how we teach writing?

Paul Fyfe Associate Professor of English

At the start of this year, ChatGPT made many people nervous about the potential impact of AI on student writing. Even though generative AI will likely affect various professions and domains, student writing became a lightning rod for concerns about cheating, as if students could press a button and produce essays or completed homework. However, the commentary hasn’t necessarily reflected students’ experiences; the reality is more complicated.

For the past few semesters, I’ve given students assignments to “cheat” on their final papers with text-generating software. In doing so, the majority of students learn (often to their surprise) as much about the limits of these technologies as their seemingly revolutionary potential. Some come away quite critical of AI, believing more firmly in their own voices. Others become curious about how to adapt these tools for different goals, or what professional or educational domains they could impact. Few, however, believe they can or should push a button to write an essay; none appreciate the assumption they will cheat.  

As AI becomes more common, so might an engaged, interdisciplinary AI literacy become a common aspect of students’ educations.

Grappling with the complexities of “cheating” also moves students beyond a focus on specific tools, which are changing stunningly fast, and toward a more generalized AI literacy. Frameworks for AI literacy are still being developed; mechanisms for teaching it are needed just as urgently.

Beyond ChatGPT, faculty and administrators must reckon with where AI literacy fits into their curricula, at levels from K-12 through higher ed. Experimenting with AI in the classroom can help faculty members learn alongside students what kinds of assignments and learning opportunities these tools might open, what critical perspectives should support them, and what guardrails we still need.  

Yet not every class can or should focus on these technologies, even if they’re likely to be affected by them. While it’s difficult to coordinate an institutional response to fast-moving technologies, generative AI seems significant enough to warrant such collective action. As AI becomes more common, so might an engaged, interdisciplinary AI literacy become a common aspect of students’ educations. 

This post was originally published in College of Humanities and Social Sciences.

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• Published: 18 January 2024

The impact of artificial intelligence on employment: the role of virtual agglomeration

• Yang Shen   ORCID: orcid.org/0000-0002-6781-6915 1 &
• Xiuwu Zhang 1  

Humanities and Social Sciences Communications volume  11 , Article number:  122 ( 2024 ) Cite this article

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Sustainable Development Goal 8 proposes the promotion of full and productive employment for all. Intelligent production factors, such as robots, the Internet of Things, and extensive data analysis, are reshaping the dynamics of labour supply and demand. In China, which is a developing country with a large population and labour force, analysing the impact of artificial intelligence technology on the labour market is of particular importance. Based on panel data from 30 provinces in China from 2006 to 2020, a two-way fixed-effect model and the two-stage least squares method are used to analyse the impact of AI on employment and to assess its heterogeneity. The introduction and installation of artificial intelligence technology as represented by industrial robots in Chinese enterprises has increased the number of jobs. The results of some mechanism studies show that the increase of labour productivity, the deepening of capital and the refinement of the division of labour that has been introduced into industrial enterprises through the introduction of robotics have successfully mitigated the damaging impact of the adoption of robot technology on employment. Rather than the traditional perceptions of robotics crowding out labour jobs, the overall impact on the labour market has exerted a promotional effect. The positive effect of artificial intelligence on employment exhibits an inevitable heterogeneity, and it serves to relatively improves the job share of women and workers in labour-intensive industries. Mechanism research has shown that virtual agglomeration, which evolved from traditional industrial agglomeration in the era of the digital economy, is an important channel for increasing employment. The findings of this study contribute to the understanding of the impact of modern digital technologies on the well-being of people in developing countries. To give full play to the positive role of artificial intelligence technology in employment, we should improve the social security system, accelerate the process of developing high-end domestic robots and deepen the reform of the education and training system.

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Introduction

Ensuring people’s livelihood requires diligence, but diligence is not scarce. Diversification, technological upgrading, and innovation all contribute to achieving the Sustainable Development Goal of full and productive employment for all (SDGs 8). Since the outbreak of the industrial revolution, human society has undergone four rounds of technological revolution, and each technological change can be regarded as the deepening of automation technology. The conflict and subsequent rebalancing of efficiency and employment are constantly being repeated in the process of replacing people with machines (Liu 2018 ; Morgan 2019 ). When people realize the new wave of human economic and social development that is created by advanced technological innovation, they must also accept the “creative destruction” brought by the iterative renewal of new technologies (Michau 2013 ; Josifidis and Supic 2018 ; Forsythe et al. 2022 ). The questions of where technology will eventually lead humanity, to what extent artificial intelligence will change the relationship between humans and work, and whether advanced productivity will lead to large-scale structural unemployment have been hotly debated. China has entered a new stage of deep integration and development of the “new technology cluster” that is represented by the internet and the real economy. Physical space, cyberspace, and biological space have become fully integrated, and new industries, new models, and new forms of business continue to emerge. In the process of the vigorous development of digital technology, its characteristics in terms of employment, such as strong absorption capacity, flexible form, and diversified job demands are more prominent, and many new occupations have emerged. The new practice of digital survival that is represented by the platform economy, sharing economy, full-time economy, and gig economy, while adapting to, leading to, and innovating the transformation and development of the economy, has also led to significant changes in employment carriers, employment forms, and occupational skill requirements (Dunn 2020 ; Wong et al. 2020 ; Li et al. 2022 ).

Artificial intelligence (AI) is one of the core areas of the fourth industrial revolution, along with the transformation of the mechanical technology, electric power technology, and information technology, and it serves to promote the transformation and upgrading of the digital economy industry. Indeed, the rapid iteration and cross-border integration of general information technology in the era of the digital economy has made a significant contribution to the stabilization of employment and the promotion of growth, but this is due only to the “employment effect” caused by the ongoing development of the times and technological progress in the field of social production. Digital technology will inevitably replace some of the tasks that were once performed by human labour. In recent years, due to the influence of China’s labour market and employment structure, some enterprises have needed help in recruiting workers. Driven by the rapid development of artificial intelligence technology, some enterprises have accelerated the pace of “machine replacement,” resulting in repetitive and standardized jobs being performed by robots. Deep learning and AI enable machines and operating systems to perform more complex tasks, and the employment prospects of enterprise employees face new challenges in the digital age. According to the Future of Jobs 2020 report released by the World Economic Forum, the recession caused by the COVID-19 pandemic and the rapid development of automation technology are changing the job market much faster than expected, and automation and the new division of labour between humans and machines will disrupt 85 million jobs in 15 industries worldwide over the next five years. The demand for skilled jobs, such as data entry, accounting, and administrative services, has been hard hit. Thanks to the wave of industrial upgrading and the vigorous development of digitalization, the recruitment demand for AI, big data, and manufacturing industries in China has maintained high growth year-on-year under the premise of macroenvironmental uncertainty during the period ranging from 2019 to 2022, and the average annual growth rate of new jobs was close to 30%. However, this growth has also aggravated the sense of occupational crisis among white-collar workers. The research shows that the agriculture, forestry, animal husbandry, fishery, mining, manufacturing, and construction industries, which are expected to adopt a high level of intelligence, face a high risk of occupational substitution, and older and less educated workers are faced with a very high risk of substitution (Wang et al. 2022 ). Whether AI, big data, and intelligent manufacturing technology, as brand-new forms of digital productivity, will lead to significant changes in the organic composition of capital and effectively decrease labour employment has yet to reach consensus. As the “pearl at the top of the manufacturing crown,” a robot is an essential carrier of intelligent manufacturing and AI technology as materialized in machinery and equipment, and it is also an important indicator for measuring a country’s high-end manufacturing industry. Due to the large number of manufacturing employees in China, the challenge of “machine substitution” to the labour market is more severe than that in other countries, and the use of AI through robots is poised to exert a substantial impact on the job market (Xie et al. 2022 ). In essence, the primary purpose of the digital transformation of industrial enterprises is to improve quality and efficiency, but the relationship between machines and workers has been distorted in the actual application of digital technology. Industrial companies use robots as an entry point, and the study delves into the impact of AI on the labour market to provide experience and policy suggestions on the best ways of coordinating the relationship between enterprise intelligent transformation and labour participation and to help realize Chinese-style modernization.

As a new general technology, AI technology represents remarkable progress in productivity. Objectively analysing the dual effects of substitution and employment creation in the era of artificial intelligence to actively integrate change and adapt to development is essential to enhancing comprehensive competitiveness and better qualifying workers for current and future work. This research is organized according to a research framework from the published literature (Luo et al. 2023 ). In this study, we used data published by the International Federation of Robotics (IFR) and take the installed density of industrial robots in China as the main indicator of AI. Based on panel data from 30 provinces in China covering the period from 2006–2020, the impact of AI technology on employment in a developing country with a large population size is empirically examined. The issues that need to be solved in this study include the following: The first goal is to examine the impact of AI on China’s labour market from the perspective of the economic behaviour of those enterprises that have adopted the use of industrial robots in production. The realistic question we expect to answer is whether the automated processing of daily tasks has led to unemployment in China during the past fifteen years. The second goal is to answer the question of how AI will continue to affect the employment market by increasing labour productivity, changing the technical composition of capital, and deepening the division of labour. The third goal is to examine how the transformation of industrial organization types in the digital economy era affects employment through digital industrial clusters or virtual clusters. The fourth goal is to test the role of AI in eliminating gender discrimination, especially in regard to whether it can improve the employment opportunities of female employees. Then, whether workers face different employment difficulties in different industry attributes is considered. The final goal is to provide some policy insights into how a developing country can achieve full employment in the face a new technological revolution in the context of a large population and many low-skilled workers.

The remainder of the paper is organized as follows. In Section Literature Review, we summarize the literature on the impact of AI on the labour market and employment and classify it from three perspectives: pessimistic, negative, and neutral. Based on a literature review, we then summarize the marginal contribution of this study. In Section Theoretical mechanism and research hypothesis, we provide a theoretical analysis of AI’s promotion of employment and present the research hypotheses to be tested. In Section Study design and data sources, we describe the data source, variable setting and econometric model. In Section Empirical analysis, we test Hypothesis 1 and conduct a robustness test and the causal identification of the conclusion. In Section Extensibility analysis, we test Hypothesis 2 and Hypothesis 3, as well as testing the heterogeneity of the baseline regression results. The heterogeneity test employee gender and industry attributes increase the relevance of the conclusions. Finally, Section Conclusions and policy implications concludes.

Literature review

The social effect of technological progress has the unique characteristics of the times and progresses through various stages, and there is variation in our understanding of its development and internal mechanism. A classic argument of labour sociology and labour economics is that technological upgrading objectively causes workers to lose their jobs, but the actual historical experience since the industrial revolution tells us that it does not cause large-scale structural unemployment (Zhang 2023a ). While neoclassical liberals such as Adam Smith claimed that technological progress would not lead to unemployment, other scholars such as Sismondi were adamant that it would. David Ricardo endorsed the “Luddite fear” in his book On Machinery, and Marx argued that technological progress can increase labour productivity while also excluding labour participation, thus leaving workers in poverty. The worker being turned ‘into a crippled monstrosity’ by modern machinery. Technology is not used to reduce working hours and improve the quality of work, rather, it is used to extend working hours and speed up work (Spencer 2023 ). According to Schumpeter’s innovation theory, within a unified complex system, the essence of technological innovation forms from the unity of positive and negative feedback and the oneness of opposites such as “revolutionary” and “destructive.” Even a tiny technological impact can cause drastic consequences. The impact of AI on employment is different from the that of previous industrial revolutions, and it is exceptional in that “machines” are no longer straightforward mechanical tools but have assumed more of a “worker” role, just as people who can learn and think tend to do (Boyd and Holton 2018 ). AI-related technologies continue to advance, the industrialization and commercialization process continues to accelerate, and the industry continues to explore the application of AI across multiple fields. Since AI was first proposed at the Dartmouth Conference in 1956, discussions about “AI replacing human labor” and “AI defeating humans” have endlessly emerged. This dynamic has increased in intensity since the emergence of ChatGPT, which has aroused people’s concerns about technology replacing the workforce. Summarizing the literature, we can find three main arguments concerning the relationship between AI and employment:

First, AI has the effect of creating and filling jobs. The intelligent manufacturing industry paradigm characterized by AI technology will assist in forming a high-quality “human‒machine cooperation” employment mode. In an enlightened society, the social state of shared prosperity benefits the lowest class of people precisely because of the advanced productive forces and higher labour efficiency created through the refinement of the division of labour. By improving production efficiency, reducing the sales price of final products, and stimulating social consumption, technological progress exerts both price effects and income effects, which in turn drive related enterprises to expand their production scale, which, in turn, increases the demand for labour (Li et al. 2021 ; Ndubuisi et al. 2021 ; Yang 2022 ; Sharma and Mishra 2023 ; Li et al. 2022 ). People habitually regard robots as competitors for human beings, but this view only represents the materialistic view of traditional machinery. The coexistence of man and machine is not a zero-sum game. When the task evolves from “cooperation for all” to “cooperation between man and machine,” it results in fewer production constraints and maximizes total factor productivity, thus creating more jobs and generating novel collaborative tasks (Balsmeier and Woerter 2019 ; Duan et al. 2023 ). At the same time, materialized AI technology can improve the total factor production efficiency in ways that are suitable for its factor endowment structure and improve the production efficiency between upstream and downstream enterprises in the industrial chain and the value chain. This increase in the efficiency of the entire market will subsequently drive the expansion of the production scale of enterprises and promote reproduction, and its synergy will promote the synchronous growth of the labour demand involving various skills, thus resulting in a creative effect (Liu et al. 2022 ). As an essential force in the fourth industrial revolution, AI inevitably affects the social status of humans and changes the structure of the labour force (Chen 2023 ). AI and machines increase labour productivity by automating routine tasks while expanding employee skills and increasing the value of work. As a result, in a machine-for-machine employment model, low-skilled jobs will disappear, while new and currently unrealized job roles will emerge (Polak 2021 ). We can even argue that digital technology, artificial intelligence, and robot encounters are helping to train skilled robots and raise their relative wages (Yoon 2023 ).

Second, AI has both a destructive effect and a substitution effect on employment. As soon as machines emerged as the means of labour, they immediately began to compete with the workers themselves. As a modern new technology, artificial intelligence is essentially humanly intelligent labour that condenses complex labour. Like the disruptive general-purpose technologies of early industrialization, automation technologies such as AI offer both promise and fear in regard to “machine replacement.” Technological progress leads to an increase in the organic composition of capital and the relative surplus population. The additional capital formed in capital accumulation comes to absorb fewer and fewer workers compared to its quantity. At the same time, old capital, which is periodically reproduced according to the new composition, will begin to increasingly exclude the workers it previously employed, resulting in severe “technological unemployment.” The development of productivity creates more free time, especially in industries such as health care, transportation, and production environment control, which have seen significant benefits from AI. In recent years, however, some industrialized countries have faced the dilemma of declining income from labour and the slow growth of total labour productivity while applying AI on a large scale (Autor 2019 ). Low-skilled and incapacitated workers enjoy a high probability of being replaced by automation (Ramos et al. 2022 ; Jetha et al. 2023 ). It is worth noting that with the in-depth development of digital technologies, such as deep learning and big data analysis, some complex, cognitive, and creative jobs that are currently considered irreplaceable in the traditional view will also be replaced by AI, which indicates that automation technology is not only a substitute for low-skilled labour (Zhao and Zhao 2017 ; Dixon et al. 2021 ; Novella et al. 2023 ; Nikitas et al. 2021 ). Among factors, AI and robotics exert a particularly significant impact on the manufacturing job market, and industry-related jobs will face a severe unemployment problem due to the disruptive effect of AI and robotics (Zhou and Chen 2022 ; Sun and Liu 2023 ). At this stage, most of the world’s economies are facing the deep integration of the digital wave in their national economy, and any work, including high-level tasks, is being affected by digitalization and AI (Gardberg et al. 2020 ). The power of AI models is growing exponentially rather than linearly, and the rapid development and rapid diffusion of technology will undoubtedly have a devastating effect on knowledge workers, as did the industrial revolution (Liu and Peng 2023 ). In particular, the development and improvement of AI-generated content in recent years poses a more significant threat to higher-level workers, such as researchers, data analysts, and product managers, than to physical labourers. White collar workers are facing unprecedented anxiety and unease (Nam 2019 ; Fossen and Sorgner 2022 ; Wang et al. 2023 ). A classic study suggests that AI could replace 47% of the 702 job types in the United States within 20 years (Frey and Osborne 2017 ). Since the 2020 epidemic, digitization has accelerated, and online and digital resources have become a must for enterprises. Many occupations are gradually moving away from humans (Wu and Yang 2022 ; Männasoo et al. 2023 ). It is obvious that the intelligent robot arm on the factory assembly line is poised to allow factory assembly line workers to exit the stage and move into history. Career guides are being replaced by mobile phone navigation software.

Third, the effect of AI on employment is uncertain, and its impact on human work does not fall into a simple “utopian” or “dystopian” scene, but rather leads to a combination of “utopia” and “dystopia” (Kolade and Owoseni 2022 ). The job-creation effects of robotics and the emergence of new jobs that result from technological change coexist at the enterprise level (Ni and Obashi 2021 ). Adopting a suitable AI operation mode can adjust for the misallocation of resources by the market, enterprises, and individuals to labour-intensive tasks, reverse the nondirectional allocation of robots in the labour sector, and promote their reallocation in the manufacturing and service industries. The size of the impact on employment through the whole society is uncertain (Fabo et al. 2017 ; Huang and Rust 2018 ; Berkers et al. 2020 ; Tschang and Almirall 2021 ; Reljic et al. 2021 ). For example, Oschinski and Wyonch ( 2017 ) claimed that those jobs that are easily replaced by AI technology in Canada account for only 1.7% of the total labour market, and they have yet to find evidence that automation technology will cause mass unemployment in the short term. Wang et al. ( 2022 ) posited that the impact of industrial robots on labour demand in the short term is mainly negative, but in the long run, its impact on employment is mainly that of job creation. Kirov and Malamin ( 2022 ) claimed that the pessimism underlying the idea that AI will destroy the jobs and quality of language workers on a large scale is unjustified. Although some jobs will be eliminated as such technology evolves, many more will be created in the long run.

In the view that modern information technology and digital technology increase employment, the literature holds that foreign direct investment (Fokam et al. 2023 ), economic systems (Bouattour et al. 2023 ), labour skills and structure (Yang 2022 ), industrial technological intensity (Graf and Mohamed 2024 ), and the easing of information friction (Jin et al. 2023 ) are important mechanisms. The research on whether AI technology crowds out jobs is voluminous, but the conclusions are inconsistent (Filippi et al. 2023 ). This paper is focused on the influence of AI on the employment scale of the manufacturing industry, examines the job creation effect of technological progress from the perspectives of capital deepening, labour refinement, and labour productivity, and systematically examines the heterogeneous impact of the adoption of industrial robots on employment demand, structure, and different industries. The marginal contributions of this paper are as follows: first, the installation density of industrial robots is used as an indicator to measure AI, and the question of whether AI has had negative effects on employment in the manufacturing sector from the perspective of machine replacement is examined. The second contribution is the analysis of the heterogeneity of AI’s employment creation effect from the perspective of gender and industry attributes and the claim that women and the employees of labour-intensive enterprises are more able to obtain additional work benefits in the digital era. Most importantly, in contrast to the literature, this paper innovatively introduces virtual agglomeration into the path mechanism of the effect of robots on employment and holds that information technologies such as the internet, big data, and the industrial Internet of Things, which rely upon AI, have reshaped the management mode and organizational structure of enterprises. Online and offline integration work together, and information, knowledge, and technology are interconnected. In the past, the job matching mode of one person, one post, and specific individuals has changed into a multiple faceted set of tasks involving one person, many posts, and many types of people. The internet platform spawned by digital technology frees the employment mode of enterprises from being limited to single enterprises and specific gathering areas. Traditional industrial geographical agglomeration has gradually evolved into virtual agglomeration, which geometrically enlarges the agglomeration effect and mechanism and enhances the spillover effect. In the online world, individual practitioners and entrepreneurs can obtain orders, receive training, connect resources and employment needs more widely and efficiently, and they can achieve higher-quality self-employment. Virtual agglomeration has become a new path by which AI affects employment. Another literature contribution is that this study used the linear regression model of the machine learning model in the robustness test part, which verified the employment creation effect of AI from the perspective of positive contribution proportion. In causal identification, this study innovatively uses the industrial feed-in price as a tool variable to analyse the causal path of AI promoting employment.

Theoretical mechanism and research hypothesis

The direct influence of ai on employment.

With advances in machine learning, big data, artificial intelligence, and other technologies, a new generation of intelligent robots that can perform routine, repetitive, and regular production tasks requiring human judgement, problem-solving, and analytical skills has emerged. Robotic process automation technology can learn and imitate the way that workers perform repeated new tasks regarding the collecting of data, running of reports, copying of data, checking of data integrity, reading, processing, and the sending of emails, and it can play an essential role in processing large amounts of data (Alan 2023 ). In the context of an informatics- and technology-oriented economy, companies are asking employees to transition into creative jobs. According to the theory of the combined task framework, the most significant advantage of the productivity effect produced by intelligent technology is creation of new demands, that is, the creation of new tasks (Acemoglu and Restrepo 2018 ). These new task packages update the existing tasks and create new task combinations with more complex technical difficulties. Although intelligent technology is widely used in various industries, it may have a substitution effect on workers and lead to technical unemployment. However, with the rise of a new round of technological innovation and revolution, high efficiency leads to the development and growth of a series of emerging industries and exerts job creation effects. Technological progress has the effect of creating new jobs. That is, such progress creates new jobs that are more in line with the needs of social development and thus increases the demand for labour (Borland and Coelli 2017 ). Therefore, the intelligent development of enterprises will come to replace their initial programmed tasks and produce more complex new tasks, and human workers in nonprogrammed positions, such as technology and knowledge, will have more comparative advantages.

Generally, the “new technology-economy” paradigm that is derived from automation machine and AI technology is affecting the breadth and depth of employment, which is manifested as follows:

It reduces the demand for coded jobs in enterprises while increasing the demand for nonprogrammed complex labour.

The development of digital technology has deepened and refined the division of labour, accelerated the service trend of the manufacturing industry, increased the employment share of the modern service industry and created many emerging jobs.

Advanced productive forces give workers higher autonomy and increased efficiency in their work, improving their job satisfaction and employment quality. As described in Das Kapital, “Although machines actually crowd out and potentially replace a large number of workers, with the development of machines themselves (which is manifested by the increase in the number of the same kind of factories or the expansion of the scale of existing factories), the number of factory workers may eventually be more than the number of handicraft workers in the workshops or handicrafts that they crowd out… It can be seen that the relative reduction and absolute increase of employed workers go hand in hand” (Li and Zhang 2022 ).

Internet information technology reduces the distance between countries in both time and space, promotes the transnational flow of production factors, and deepens the international division of labour. The emergence of AI technology leads to the decline of a country’s traditional industries and departments. Under the new changes to the division of labour, these industries and departments may develop in late-developing countries and serve to increase their employment through international labour export.

From a long-term perspective, AI will create more jobs through the continuous expansion of the social production scale, the continuous improvement of production efficiency, and the more detailed industrial categories that it engenders. With the accumulation of human capital under the internet era, practitioners are gradually becoming liberated from heavy and dangerous work, and workers’ skills and job adaptability will undergo continuous improvement. The employment creation and compensation effects caused by technological and industrial changes are more significant than the substitution effects (Han et al. 2022 ). Accordingly, the article proposes the following two research hypotheses:

Hypothesis 1 (H1): AI increases employment .

Hypothesis 2 (H2): AI promotes employment by improving labour productivity, deepening capital, and refining the division of labour .

Role of virtual agglomeration

The research on economic geography and “new” economic geography agglomeration theory focuses on industrial agglomeration in the traditional sense. This model is a geographical agglomeration model that depends on spatial proximity from a geographical perspective. Assessing the role of externalities requires a particular geographical scope, as it has both physical and scope limitations. Virtual agglomeration transcends Marshall’s theory of economies of scale, which is not limited to geographical agglomeration from the perspective of natural territory but rather takes on more complex and multidimensional forms (such as virtual clusters, high-tech industrial clusters, and virtual business circles). Under the influence of a new generation of digital technology that is characterized by big data, the Internet of Things, and the industrial internet, the digital, intelligent, and platform transformation trend is prominent in some industries and enterprises, and industrial digitalization and digital industrialization jointly promote industrial upgrading. The innovation of information technology leads to “distance death” (Schultz 1998 ). With the further materialization of digital and networked services of enterprises, the trading mode of digital knowledge and services, such as professional knowledge, information combination, cultural products, and consulting services, has transitioned from offline to digital trade, and the original geographical space gathering mode between enterprises has gradually evolved into a virtual network gathering that places the real-time exchange of data and information as its core (Wang et al. 2018 ). Tan and Xia ( 2022 ) stated that virtual agglomeration geometrically magnifies the social impact of industrial agglomeration mechanisms and agglomeration effects, and enterprises in the same industry and their upstream and downstream affiliated enterprises can realize low-cost long-distance transactions, services, and collaborative production through digital trade, resulting in large-scale zero-distance agglomeration along with neighbourhood-style production, service, circulation, and consumption. First, the knowledge and information underlying the production, design, research and development, organization, and trading of all kinds of enterprises are increasingly being completed by digital technology. The tacit knowledge that used to require face-to-face communication has become codable, transmissible, and reproducible under digital technology. Tacit knowledge has gradually become explicit, and knowledge spillover and technology diffusion have become more pronounced, which further leads to an increase in the demand for unconventional task labour (Zhang and Li 2022 ). Second, the cloud platform causes the labour pool effect of traditional geographical agglomeration to evolve into the labour “conservation land” of virtual agglomeration, and employment is no longer limited to the internal organization or constrained within a particular regional scope. Digital technology allows enterprises to hire “ghost workers” for lower wages to compensate for the possibility of AI’s “last mile.” Information technology and network platforms seek connections with all social nodes, promoting the time and space for work in a way that transcends standardized fixed frameworks. At the same time, joining or quitting work tasks, indirectly increasing the temporary and transitional nature of work and forming a decentralized management organization model of supplementary cooperation, social networks, industry experts, and skilled labour all become more convenient for workers (Wen and Liu 2021 ). With a mobile phone and a computer, labourers worldwide can create value for enterprises or customers, and the forms of labour are becoming more flexible and diverse. Workers can provide digital real-time services to employers far away from their residence, and they can also obtain flexible employment information and improve their digital skills through the leveraging of digital resources, resulting in the odd-job economy, crowdsourcing economy, sharing economy, and other economic forms. Finally, the network virtual space can accommodate almost unlimited enterprises simultaneously. In the commercial background of digital trade, while any enterprise can obtain any intermediate supply in the online market, its final product output can instantly become the intermediate input of other enterprises. Therefore, enterprises’ raw material supply and product sales rely on the whole market. At this time, the market scale effect of intermediate inputs can be infinitely amplified, as it is no longer confined to the limited space of geographical agglomeration (Duan and Zhang 2023 ). Accordingly, the following research hypothesis is proposed:

Hypothesis 3 (H3): AI promotes employment by improving the VA of enterprises .

Study design and data sources

Variable setting, explained variable.

Employment scale (ES). Compared with the agriculture and service industry, the industrial sector accommodates more labour, and robot technology is mainly applied in the industrial sector, which has the greatest demand shock effect on manufacturing jobs. In this paper, we select the number of employees in manufacturing cities and towns as the proxy variable for employment scale.

Core explanatory variable

Artificial intelligence (AI). Emerging technologies endow industrial robots with more complete technical attributes, which increases their ability to act as human beings in many work projects, enabling them to either independently complete production tasks or to assist humans in completing such tasks. This represents an important form of AI technology embedded into machinery and equipment. In this paper, the installation density of industrial robots is selected as the proxy variable for AI. Robot data mainly come from the number of robots installed in various industries at various national levels as published by the International Federation of Robotics (IFR). Because the dataset published by the IFR provides the dataset at the national-industry level and its industry classification standards are significantly different from those in China, the first lessons for this paper are drawn from the practices of Yan et al. ( 2020 ), who matches the 14 manufacturing categories published by the IFR with the subsectors in China’s manufacturing sector, and then uses the mobile share method to merge and sort out the employment numbers of various industries in various provinces. First, the national subsector data provided by the IFR are matched with the second National Economic Census data. Next, the share of employment in different industries to the total employment in the province is used to develop weights and decompose the industry-level robot data into the local “provincial-level industry” level. Finally, the application of robots in various industries at the provincial level is summarized. The Bartik shift-share instrumental variable is now widely used to measure robot installation density at the city (province) level (Wu 2023 ; Yang and Shen, 2023 ; Shen and Yang 2023 ). The calculation process is as follows:

In Eq. ( 1 ), N is a collection of manufacturing industries, Robot it is the robot installation density of province i in year t, \({{{\mathrm{employ}}}}_{{{{\mathrm{ij}}}},{{{\mathrm{t}}}} = 2006}\) is the number of employees in industry j of province i in 2006, \({{{\mathrm{employ}}}}_{{{{\mathrm{i}}}},{{{\mathrm{t}}}} = 2006}\) is the total number of employees in province i in 2006, and \({{{\mathrm{Robot}}}}_{{{{\mathrm{jt}}}}}{{{\mathrm{/employ}}}}_{{{{\mathrm{i}}}},{{{\mathrm{t}}}} = 2006}\) represents the robot installation density of each year and industry level.

Mediating variables

Labour productivity (LP). According to the definition and measurement method proposed by Marx’s labour theory of value, labour productivity is measured by the balance of the total social product minus the intermediate goods and the amount of labour consumed by the pure production sector. The specific calculation process is \(AL = Y - k/l\) , where Y represents GDP, l represents employment, k represents capital depreciation, and AL represents labour productivity. Capital deepening (CD). The per capita fixed capital stock of industrial enterprises above a designated size is used in this study as a proxy variable for capital deepening. The division of labour refinement (DLR) is refined and measured by the number of employees in producer services. Virtual agglomeration (VA) is mainly a continuation of the location entropy method in the traditional industrial agglomeration measurement idea, and weights are assigned according to the proportion of the number of internet access ports in the country. Because of the dependence of virtual agglomeration on digital technology and network information platforms, the industrial agglomeration degree of each region is first calculated in this paper by using the number of information transmissions, computer services, and software practitioners and then multiplying that number by the internet port weight. The specific expression is \(Agg_{it} = \left( {M_{it}/M_t} \right)/\left( {E_{it}/E_t} \right) \times \left( {Net_{it}/Net_t} \right)\) , where \(M_{it}\) represents the number of information transmissions, computer services and software practitioners in region i in year t, \(M_t\) represents the total number of national employees in this industry, \(E_{it}\) represents the total number of employees in region i, \(E_t\) represents the total number of national employees, \(Net_{it}\) represents the number of internet broadband access ports in region i, and \(Net_t\) represents the total number of internet broadband access ports in the country. VA represents the degree of virtual agglomeration.

Control variables

To avoid endogeneity problems caused by unobserved variables and to obtain more accurate estimation results, seven control variables were also selected. Road accessibility (RA) is measured by the actual road area at the end of the year. Industrial structure (IS) is measured by the proportion of the tertiary industry’s added value and the secondary industry’s added value. The full-time equivalent of R&D personnel is used to measure R&D investment (RD). Wage cost (WC) is calculated using city average salary as a proxy variable; Marketization (MK) is determined using Fan Gang marketization index as a proxy variable; Urbanization (UR) is measured by the proportion of the urban population to the total population at the end of the year; and the proportion of general budget expenditure to GDP is used to measure Macrocontrol (MC).

Econometric model

To investigate the impact of AI on employment, based on the selection and definition of the variables detailed above and by mapping the research ideas to an empirical model, the following linear regression model is constructed:

In Eq. ( 2 ), ES represents the scale of manufacturing employment, AI represents artificial intelligence, and subscripts t, i and m represent time t, individual i and the m th control variable, respectively. \(\mu _i\) , \(\nu _t\) and \(\varepsilon _{it}\) represent the individual effect, time effect and random disturbance terms, respectively. \(\delta _0\) is the constant term, a is the parameter to be fitted, and Control represents a series of control variables. To further test whether there is a mediating effect of mechanism variables in the process of AI affecting employment, only the influence of AI on mechanism variables is tested in the empirical part according to the modelling process and operational suggestions of the intermediary effects as proposed by Jiang ( 2022 ) to overcome the inherent defects of the intermediary effects. On the basis of Eq. ( 2 ), the following econometric model is constructed:

In Eq. ( 3 ), Media represents the mechanism variable. β 1 represents the degree of influence of AI on mechanism variables, and its significance and symbolic direction still need to be emphasized. The meanings of the remaining symbols are consistent with those of Eq. ( 2 ).

Data sources

Following the principle of data availability, the panel data of 30 provinces (municipalities and autonomous regions) in China from 2006 to 2020 (samples from Tibet and Hong Kong, Macao, and Taiwan were excluded due to data availability) were used as statistical investigation samples. The raw data on the installed density of industrial robots and the number of workers in the manufacturing industry come from the International Federation of Robotics and the China Labour Statistics Yearbook. The original data for the remaining indicators came from the China Statistical Yearbook, China Population and Employment Statistical Yearbook, China’s Marketization Index Report by Province (2021), the provincial and municipal Bureau of Statistics, and the global statistical data analysis platform of the Economy Prediction System (EPS). The few missing values are supplemented through linear interpolation. It should be noted that although the IFR has yet to release the number of robots installed at the country-industry level in 2020, it has published the overall growth rate of new robot installations, which is used to calculate the robot stock in 2020 for this study. The descriptive statistical analysis of relevant variables is shown in Table 1 .

Empirical analysis

To reduce the volatility of the data and address the possible heteroscedasticity problem, all the variables are located. The results of the Hausmann test and F test both reject the null hypothesis at the 1% level, indicating that the fixed effect model is the best-fitting model. Table 2 reports the fitting results of the baseline regression.

As shown in Table 2 , the results of the two-way fixed-effect (TWFE) model displayed in Column (5) show that the fitting coefficient of AI on employment is 0.989 and is significant at the 1% level. At the same time, the fitting results of other models show that the impact of AI on employment is significantly positive. The results confirm that the effect of AI on employment is positive and the effect of job creation is greater than the effect of destruction, and these conclusions are robust, thus verifying the employment creation mechanism of technological progress. Research Hypothesis 1 (H1) is supported. The new round of scientific and technological revolution represented by artificial intelligence involves the upgrading of traditional industries, the promotion of major changes in the economy and society, the driving of rapid development of the “unmanned economy,” the spawning a large number of new products, new technologies, new formats, and new models, and the provision of more possibilities for promoting greater and higher quality employment. Classical and neoclassical economics view the market mechanism as a process of automatic correction that can offset the job losses caused by labour-saving technological innovation. Under the premise of the “employment compensation” theory, the new products, new models, and new industrial sectors created by the progress of AI technology can directly promote employment. At the same time, the scale effect caused by advanced productivity results in lower product prices and higher worker incomes, which drives increased demand and economic growth, increasing output growth and employment (Ge and Zhao 2023 ). In conjunction with the empirical results of this paper, we have reason to believe that enterprises adopt the strategy of “machine replacement” to replace procedural and repetitive labour positions in the pursuit of high efficiency and high profits. However, AI improves not only enterprises’ production efficiency but also their production capacity and scale economy. To occupy a favourable share of market competition, enterprises expand the scale of reproduction. At this point, new and more complex tasks continue to emerge, eventually leading companies to hire more labour. At this stage, robot technology and application in developing countries are still in their infancy. Whether regarding the application scenario or the application scope of robots, the automation technology represented by industrial robots has not yet been widely promoted, which increases the time required for the automation technology to completely replace manual tasks, so the destruction effect of automation technology on jobs is not apparent. The fundamental market situation of the low cost of China’s labour market drives enterprises to pay more attention to technology upgrading and efficiency improvement when introducing industrial robots. The implementation of the machine replacement strategy is mainly caused by the labour shortage driven by high work intensity, high risk, simple process repetition, and poor working conditions. The intelligent transformation of enterprises points to more than the simple saving of labour costs (Dixon et al. 2021 ).

Robustness test

The above results show that the effect of AI on job creation is greater than the effect of substitution and the overall promotion of enterprises for the enhancement of employment demand. To verify the robustness of the benchmark results, the following three means of verifying the results are adopted in this study. First, we replace the explained variables. In addition to industrial manufacturing, robots are widely used in service industries, such as medical care, finance, catering, and education. To reflect the dynamic change relationship between the employment share of the manufacturing sector and the employment number of all sectors, the absolute number of manufacturing employees is replaced by the ratio of the manufacturing industry to all employment numbers. The second means is increasing the missing variables. Since many factors affect employment, this paper considers the living cots, human capital, population density, and union power in the basic regression model. The impact of these variables on employment is noticeable; for example, the existence of trade unions improves employee welfare and the working environment but raises the entry barrier for workers in the external market. The new missing variables are the average selling price of commercial and residential buildings, urban population density (person/square kilometre), nominal human capital stock, and the number of grassroots trade union organizations in the China Human Capital Report 2021 issued by Central University of Finance and Economics, which are used as proxy variables. The third means involves the use of linear regression (the gradient descent method) in machine learning regression to calculate the importance of AI to the increase in employment size. The machine learning model has a higher goodness of fit and fitting effect on the predicted data, and its mean square error and mean absolute error are more minor (Wang Y et al. 2022 ).

As seen from the robustness part of Table 3 , the results of Method 1 show that AI exerts a positive impact on the employment share in the manufacturing industry; that is, AI can increase the proportion of employment in the manufacturing industry, the use of AI creates more derivative jobs for the manufacturing industry, and the demand for the labour force of enterprises further increases. The results of method 2 show that after increasing the number of control variables, the influence of robots on employment remains significantly positive, indicating no social phenomenon of “machine replacement.” The results of method 3 show that the weight of AI is 84.3%, indicating that AI can explain most of the increase in the manufacturing employment scale and has a positive promoting effect. The above three methods confirm the robustness of the baseline regression results.

Endogenous problem

Although further control variables are used to alleviate the endogeneity problem caused by missing variables to the greatest extent possible, the bidirectional causal relationship between labour demand and robot installation (for example, enterprises tend to passively adopt the machine replacement strategy in the case of labour shortages and recruitment difficulties) still threatens the accuracy of the statistical inference results in this paper. To eliminate the potential endogeneity problem of the model, the two-stage least squares method (2SLS) was applied. In general, the cost factor that enterprises need to consider when introducing industrial robots is not only the comparative advantage between the efficiency cost of machinery and the costs of equipment and labour wages but also the cost of electricity to maintain the efficient operation of machinery and equipment. Changes in industrial electricity prices indicate that the dynamic conditions between installing robots and hiring workers have changed, and decision-makers need to reweigh the costs and profits of intelligent transformation. Changes in industrial electricity prices can impact the demand for labour by enterprises; this path does not directly affect the labour market but is rather based on the power consumption, work efficiency, and equipment prices of robots. Therefore, industrial electricity prices are exogenous relative to employment, and the demand for robots is correlated.

Electricity production and operation can be divided into power generation, transmission, distribution, and sales. China has realized the integration of exports and distribution, so there are two critical prices in practice: on-grid and sales tariffs (Yu and Liu 2017 ). The government determines the on-grid tariff according to different cost-plus models, and its regulatory policy has roughly proceeded from that of principal and interest repayment, through operating period pricing, to benchmark pricing. The sales price (also known as the catalogue price) is the price of electric energy sold by power grid operators to end users, and its price structure is formed based on the “electric heating price” that was implemented in 1976. There is differentiated pricing between industrial and agricultural electricity. Generally, government departments formulate on-grid tariffs, integrating the interests of power plants, grid enterprises, and end users. As China’s thermal power installed capacity accounts for more than 70% of the installed capacity of generators, the price of coal becomes an essential factor affecting the price of industrial internet access. The pricing strategy for electricity sales is not determined by market-oriented transmission and distribution electricity price, on-grid electricity price, or tax but rather by the goal of “stable growth and ensuring people’s livelihood” (Tang and Yang 2014 ). The externality of the feed-in price is more robust, so the paper chooses the feed-in price as an instrumental variable.

It can be seen from Table 3 that the instrumental variables in the first stage positively affect the robot installation density at the level of 1%. Meanwhile, the results of the validity test of the instrumental variables show that there are no weak instrumental variables or unidentifiable problems with this variable, thus satisfying the principle of correlation and exclusivity. The second-stage results show that robots still positively affect the demand for labour at the 1% level, but the fitting coefficient is smaller than that of the benchmark regression model. In summary, the results of fitting the calculation with the causal inference paradigm still support the conclusion that robots create more jobs and increase the labour demand of enterprises.

Extensibility analysis

Robot adoption and gender bias.

The quantity and quality of labour needed by various industries in the manufacturing sector vary greatly, and labour-intensive and capital-intensive industries have different labour needs. Over the past few decades, the demand for female employees has grown. Female employees obtain more job opportunities and better salaries today (Zhang et al. 2023 ). Female employees may benefit from reducing the content of manual labour jobs, meaning that further study of AI heterogeneity from the perspective of gender bias may be needed. As seen from Table 4 , AI has a significant positive impact on the employment of both male and female practitioners, indicating that AI technology does not have a heterogeneous effect on the dynamic gender structure. By comparing the coefficients of the two (the estimated results for men and those for women), it can be found that robots have a more significant promotion effect on female employees. AI has significantly improved the working environment of front-line workers, reduced the level of labour intensity, enabled people to free themselves of dirty and heavy work tasks, and indirectly improved the job adaptability of female workers. Intellectualization increases the flexibility of the time, place, and manner of work for workers, correspondingly improves the working freedom of female workers, and alleviates the imbalance in the choice between family and career for women to a certain extent (Lu et al. 2023 ). At the same time, women are born with the comparative advantage of cognitive skills that allow them to pay more nuanced attention to work details. By introducing automated technology, companies are increasing the demand for cognitive skills such as mental labour and sentiment analysis, thus increasing the benefits for female workers (Wang and Zhang 2022 ). Flexible employment forms, such as online car hailing, community e-commerce, and online live broadcasting, provide a broader stage for women’s entrepreneurship and employment. According to the “Didi Digital Platform and Female Ecology Research Report”, the number of newly registered female online taxi drivers in China has exceeded 265,000 since 2020, and approximately 60 percent of the heads of the e-commerce platform, Orange Heart, are women.

Industry heterogeneity

Given the significant differences in the combination of factors across the different industries in China’s manufacturing sector, there is also a significant gap in the installation density of robots; even compared to AI density, in industries with different production characteristics, indicating that there may be an opposite employment phenomenon at play. According to the number of employees and their salary level, capital stock, R&D investment, and patent technology, the manufacturing industry is divided into labour-intensive (LI), capital-intensive (CI), and technology-intensive (TI) industries.

As seen from the industry-specific test results displayed in Table 4 , the impact of AI on employment in the three attribute industries is significantly positive, which is consistent with the results of Beier et al. ( 2022 ). In contrast, labour-intensive industries can absorb more workers, and industry practitioners are better able to share digital dividends from these new workers, which is generally in line with expectations (in the labour-intensive case, the regression coefficient of AI on employment is 0.054, which is significantly larger than the regression coefficient of the other two industries). This conclusion shows that enterprises use AI to replace the labour force of procedural and process-based positions in pursuit of cost-effective performance. However, the scale effect generated by improving enterprise production efficiency leads to increased labour demand, namely, productivity and compensation effects. For example, AGV-handling robots are used to replace porters in monotonous and repetitive high-intensity work, thus realizing the uncrewed operation of storage links and the automatic handling of goods, semifinished products, and raw materials in the production process. This reduces the cost of goods storage while improving the efficiency of logistics handling, increasing the capital investment of enterprises in the expansion of market share and extension of the industrial chain.

Mechanism test

To reveal the path mechanism through which AI affects employment, in combination with H2 and H3 and the intermediary effect model constructed with Eq. ( 3 ), the TWFE model was used to fit the results shown in Table 5 .

It can be seen from Table 5 that the fitting coefficients of AI for capital deepening, labour productivity, and division of labour are 0.052, 0.071, and 0.302, respectively, and are all significant at the 1% level, indicating that AI can promote employment through the above three mechanisms, and thus research Hypothesis 2 (H2) is supported. Compared with the workshop and handicraft industry, machine production has driven incomparably broad development in the social division of labour. Intelligent transformation helps to open up the internal and external data chain, improve the combination of production factors, reduce costs and increase efficiency to enable the high-quality development of enterprises. At the macro level, the impact of robotics on social productivity, industrial structure, and product prices affects the labour demand of enterprises. At the micro level, robot technology changes the employment carrier, skill requirements, and employment form of labour and impacts the matching of labour supply and demand. The combination of the price and income effects can drive the impact of technological progress on employment creation. While improving labour productivity, AI technology reduces product production costs. In the case of constant nominal income, the market increases the demand for the product, which in turn drives the expansion of the industrial scale and increases output, resulting in an increase in the demand for labour. At the same time, the emergence of robotics has refined the division of labour. Most importantly, the development of AI technology results in productivity improvements that cannot be matched by pure labour input, which not only enables 24 h automation but also reduces error rates, improves precision, and accelerates production speeds.

Table 5 also shows that the fitting coefficient of AI to virtual agglomeration is 0.141 and significant at the 5% level, indicating that AI and digital technology can promote employment by promoting the agglomeration degree of enterprises in the cloud and network. Research Hypothesis 3 is thus supported. Industrial internet, AI, collaborative robots, and optical fidelity information transmission technology are necessary for the future of the manufacturing industry, and smart factories will become the ultimate direction of manufacturing. Under the intelligent manufacturing model, by leveraging cloud links, industrial robots, and the technological depth needed to achieve autonomous management, the proximity advantage of geographic spatial agglomeration gradually begins to fade. The panconnective features of digital technology break through the situational constraints of work, reshaping the static, linear, and demarcated organizational structure and management modes of the industrial era and increasingly facilitates dynamic, network-based, borderless organizational forms, despite the fact that traditional work tasks can be carried out on a broader network platform employing online office platforms and online meetings. While promoting cost reduction and efficiency increase, such connectivity also creates new occupations that rely on this network to achieve efficient virtual agglomeration. On the other hand, robot technology has also broken the fixed connection between people and jobs, and the previous post matching mode of one person and one specific individual has gradually evolved into an organizational structure involving multiple posts and multiple people, thus providing more diverse and inclusive jobs for different groups.

Conclusions and policy implications

Research conclusion.

The decisive impact of digitization and automation on the functioning of all society’s social subsystems is indisputable. Technological progress alone does not impart any purpose to technology, and its value (consciousness) can only be defined by its application in the social context in which it emerges (Rakowski et al. 2021 ). The recent launch of the intelligent chatbot ChatGPT by the US artificial intelligence company OpenAI, with its powerful word processing capabilities and human-computer interaction, has once again sparked global concerns about its potential impact on employment in related industries. Automation technology represented by intelligent manufacturing profoundly affects the labour supply and demand map and significantly impacts economic and social development. The application of industrial robots is a concrete reflection of the integration of AI technology and industry, and its widespread promotion and popularization in the manufacturing field have resulted in changes in production methods and exerted impacts on the labour market. In this paper, the internal mechanism of AI’s impact on employment is first delineated and then empirical tests based on panel data from 30 provinces (municipalities and autonomous regions, excluding Hong Kong, Macao, Taiwan, and Xizang) in China from 2006 to 2020 are subsequently conducted. As mentioned in relation to the theory of “employment compensation,” the research described in this paper shows that the overall impact of AI on employment is positive, revealing a pronounced job creation effect, and the impact of automation technology on the labour market is mainly positively manifested as “icing on the cake.” Our conclusion is consistent with the literature (Sharma and Mishra 2023 ; Feng et al. 2024 ). This conclusion remains after replacing variables, adding missing variables, and controlling for endogeneity problems. The positive role of AI in promoting employment does not have exert opposite effects resulting from gender and industry differences. However, it brings greater digital welfare to female practitioners and workers in labour-intensive industries while relatively reducing the overall proportion of male practitioners in the manufacturing industry. Mechanism analysis shows that AI drives employment through mechanisms that promote capital deepening, the division of labour, and increased labour productivity. The digital trade derived from digital technology and internet platforms has promoted the transformation of traditional industrial agglomeration into virtual agglomeration, the constructed network flow space system is more prone to the free spillover of knowledge, technology, and creativity, and the agglomeration effect and agglomeration mechanism are amplified by geometric multiples. Industrial virtual agglomeration has become a new mechanism and an essential channel through which AI promotes employment, which helps to enhance labour autonomy, improve job suitability and encourage enterprises to share the welfare of labour among “cultivation areas.”

Policy implications

Technology is neutral, and its key lies in its use. Artificial intelligence technology, as an open new general technology, represents significant progress in productivity and is an essential driving force with the potential to boost economic development. However, it also inevitably poses many potential risks and social problems. This study helps to clarify the argument that technology replaces jobs by revealing the impact of automation technology on China’s labour market at the present stage, and its findings alleviate the social anxiety caused by the fear of machine replacement. According to the above research conclusions, the following valuable implications can be obtained.

Investment in AI research and development should be increased, and the high-end development of domestic robots should be accelerated. The development of AI has not only resulted in the improvement of production efficiency but has also triggered a change in industrial structure and labour structure, and it has also generated new jobs as it has replaced human labour. Currently, the impact of AI on employment in China is positive and helps to stabilize employment. Speeding up the development of the information infrastructure, accelerating the intelligent upgrade of the traditional physical infrastructure, and realizing the inclusive promotion of intelligent infrastructure are necessary to ensure efficient development. 5G technology and the development dividend of the digital economy can be used to increase the level of investment in new infrastructure such as cloud computing, the Internet of Things, blockchain, and the industrial internet and to improve the level of intelligent application across the industry. We need to implement the intelligent transformation of old infrastructure, upgrade traditional old infrastructure to smart new infrastructure, and digitally transform traditional forms of infrastructure such as power, reservoirs, rivers, and urban sewer pipes through the employment of sensors and access algorithms to solve infrastructure problems more intelligently. Second, the diversification and agglomeration of industrial lines are facilitated through the transformation of industrial intelligence and automation. At the same time, it is necessary to speed up the process of industrial intelligence and cultivate the prospects of emerging industries and employment carriers, particularly in regard to the development and growth of emerging producer services. The development of domestic robots should be task-oriented and application-oriented, should adhere to the effective transformation of scientific and technological achievements under the guidance of the development of the service economy. A “1 + 2 + N” collaborative innovation ecosystem should be constructed with a focus on cultivating, incubating, and supporting critical technological innovation in each subindustry of the manufacturing industry, optimizing the layout, and forming a matrix multilevel achievement transformation service. We need to improve the mechanisms used for complementing research and production, such as technology investment and authorization. To move beyond standard robot system development technology, the research and development of bionic perception and knowledge, as well as other cutting-edge technologies need to be developed to overcome the core technology “bottleneck” problem.

It is suggested that government departments improve the social security system and stabilize employment through multiple channels. The first channel is the evaluation and monitoring of the potential destruction of the low-end labour force by AI, enabled through the cooperation of the government and enterprises, to build relevant information platforms, improve the transparency of the labour market information, and reasonably anticipate structural unemployment. Big data should be fully leveraged, a sound national employment information monitoring platform should be built, real-time monitoring of the dynamic changes in employment in critical regions, fundamental groups, and key positions should be implemented, employment status information should be released, and employment early warning, forecasting, and prediction should be provided. Second, the backstop role of public service, including human resources departments and social security departments at all levels, should improve the relevant social security system in a timely manner. A mixed-guarantee model can be adopted for the potential unemployed and laws and regulations to protect the legitimate rights and interests of entrepreneurs and temporary employees should be improved. We can gradually expand the coverage of unemployment insurance and basic living allowances. For the extremely poor, unemployed or extreme labour shortage groups, public welfare jobs or special subsidies can be used to stabilize their basic lifestyles. The second is to understand the working conditions of the bottom workers at the grassroots level in greater depth, strengthen the statistical investigation and professional evaluation of AI technology and related jobs, provide skills training, employment assistance, and unemployment subsidies for workers who are unemployed due to the use of AI, and encourage unemployed groups to participate in vocational skills training to improve their applicable skillsets. Workers should be encouraged to use their fragmented time to participate in the gig and sharing economies and achieve flexible employment according to dominant conditions. Finally, a focus should be established on the impact of AI on the changing demand for jobs in specific industries, especially transportation equipment manufacturing and communications equipment, computers, and other electronic equipment manufacturing.

It is suggested that education departments promote the reform of the education and training system and deepen the coordinated development of industry-university research. Big data, the Internet of Things, and AI, as new digital production factors, have penetrated daily economic activities, driving industrial changes and changes in the supply and demand dynamics of the job market. Heterogeneity analysis results confirmed that AI imparts a high level of digital welfare for women and workers in labour-intensive industrial enterprises, but to stimulate the spillover of technology dividends in the whole society, it is necessary to dynamically optimize human capital and improve the adaptability of man-machine collaborative work; otherwise, the disruptive effect of intelligent technology on low-end, routine and programmable work will be obscured. AI has a creativity promoting effect on irregular, creative, and stylized technical positions. Hence, the contradiction between supply and demand in the labour market and the slow transformation of the labour skill structure requires attention. The relevant administrative departments of the state should take the lead in increasing investment in basic research and forming a scientific research division system in which enterprises increase their levels of investment in experimental development and multiple subjects participate in R&D. Relevant departments should clarify the urgent need for talent in the digital economy era, deepen the reform of the education system as a guide, encourage all kinds of colleges and universities to add related majors around AI and big data analysis, accelerate the research on the skill needs of new careers and jobs, and establish a lifelong learning and employment training system that meets the needs of the innovative economy and intelligent society. We need to strengthen the training of innovative, technical, and professional technical personnel, focus on cultivating interdisciplinary talent and AI-related professionals to improve worker adaptability to new industries and technologies, deepen the adjustment of the educational structure, increase the skills and knowledge of perceptual, creative, and social abilities of the workforce, and cultivate the skills needed to perform complex jobs in the future that are difficult to replace by AI. The lifelong education and training system should be improved, and enterprise employees should be encouraged to participate in vocational skills training and cultural knowledge learning through activities such as vocational and technical schools, enterprise universities, and personnel exchanges.

Research limitations

The study used panel data from 30 provinces in China from 2006 to 2020 to examine the impact of AI on employment using econometric models. Therefore, the conclusions obtained in this study are only applicable to the economic reality in China during the sample period. There are three shortcomings in this study. First, only the effect and mechanism of AI in promoting employment from a macro level are investigated in this study, which is limited by the large data particles and small sample data that are factors that reduce the reliability and validity of statistical inference. The digital economy has grown rapidly in the wake of the COVID-19 pandemic, and the related industrial structures and job types have been affected by sudden public events. An examination of the impact of AI on employment based on nearly three years of micro-data (particularly the data obtained from field research) is urgent. When conducting empirical analysis, combining case studies of enterprises that are undergoing digital transformation is very helpful. Second, although the two-way fixed effect model and instrumental variable method can reveal conclusions regarding causality to a certain extent, these conclusions are not causal inference in the strict sense. Due to the lack of good policy pilots regarding industrial robots and digital parks, the topic cannot be thoroughly evaluated for determining policy and calculating resident welfare. In future research, researchers can look for policies and systems such as big data pilot zones, intelligent industrial parks, and digital economy demonstration zones to perform policy evaluations through quasinatural experiments. The use of difference in differences (DID), regression discontinuity (RD), and synthetic control method (SCM) to perform regression is beneficial. In addition, the diffusion effect caused by introducing and installing industrial robots leads to the flow of labour between regions, resulting in a potential spatial spillover effect. Although the spatial econometric model is used above, it is mainly used as a robustness test, and the direct effect is considered. This paper has yet to discuss the spatial effect from the perspective of the spatial spillover effect. Last, it is important to note that the digital infrastructure, workforce, and industrial structure differ from country to country. The study focused on a sample of data from China, making the findings only partially applicable to other countries. Therefore, the sample size of countries should be expanded in future studies, and the possible heterogeneity of AI should be explored and compared by classifying different countries according to their stage of development.

Data availability

The data generated during and/or analyzed during the current study are provided in Supplementary File “database”.

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This work was financially supported by the Natural Science Foundation of Fujian Province (Grant No. 2022J01320).

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Shen, Y., Zhang, X. The impact of artificial intelligence on employment: the role of virtual agglomeration. Humanit Soc Sci Commun 11 , 122 (2024). https://doi.org/10.1057/s41599-024-02647-9

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The impact of artificial intelligence on human society and bioethics

Michael cheng-tek tai.

Department of Medical Sociology and Social Work, College of Medicine, Chung Shan Medical University, Taichung, Taiwan

Artificial intelligence (AI), known by some as the industrial revolution (IR) 4.0, is going to change not only the way we do things, how we relate to others, but also what we know about ourselves. This article will first examine what AI is, discuss its impact on industrial, social, and economic changes on humankind in the 21 st century, and then propose a set of principles for AI bioethics. The IR1.0, the IR of the 18 th century, impelled a huge social change without directly complicating human relationships. Modern AI, however, has a tremendous impact on how we do things and also the ways we relate to one another. Facing this challenge, new principles of AI bioethics must be considered and developed to provide guidelines for the AI technology to observe so that the world will be benefited by the progress of this new intelligence.

W HAT IS ARTIFICIAL INTELLIGENCE ?

Artificial intelligence (AI) has many different definitions; some see it as the created technology that allows computers and machines to function intelligently. Some see it as the machine that replaces human labor to work for men a more effective and speedier result. Others see it as “a system” with the ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation [ 1 ].

Despite the different definitions, the common understanding of AI is that it is associated with machines and computers to help humankind solve problems and facilitate working processes. In short, it is an intelligence designed by humans and demonstrated by machines. The term AI is used to describe these functions of human-made tool that emulates the “cognitive” abilities of the natural intelligence of human minds [ 2 ].

Along with the rapid development of cybernetic technology in recent years, AI has been seen almost in all our life circles, and some of that may no longer be regarded as AI because it is so common in daily life that we are much used to it such as optical character recognition or the Siri (speech interpretation and recognition interface) of information searching equipment on computer [ 3 ].

D IFFERENT TYPES OF ARTIFICIAL INTELLIGENCE

From the functions and abilities provided by AI, we can distinguish two different types. The first is weak AI, also known as narrow AI that is designed to perform a narrow task, such as facial recognition or Internet Siri search or self-driving car. Many currently existing systems that claim to use “AI” are likely operating as a weak AI focusing on a narrowly defined specific function. Although this weak AI seems to be helpful to human living, there are still some think weak AI could be dangerous because weak AI could cause disruptions in the electric grid or may damage nuclear power plants when malfunctioned.

The new development of the long-term goal of many researchers is to create strong AI or artificial general intelligence (AGI) which is the speculative intelligence of a machine that has the capacity to understand or learn any intelligent task human being can, thus assisting human to unravel the confronted problem. While narrow AI may outperform humans such as playing chess or solving equations, but its effect is still weak. AGI, however, could outperform humans at nearly every cognitive task.

Strong AI is a different perception of AI that it can be programmed to actually be a human mind, to be intelligent in whatever it is commanded to attempt, even to have perception, beliefs and other cognitive capacities that are normally only ascribed to humans [ 4 ].

In summary, we can see these different functions of AI [ 5 , 6 ]:

• Automation: What makes a system or process to function automatically
• Machine learning and vision: The science of getting a computer to act through deep learning to predict and analyze, and to see through a camera, analog-to-digital conversion and digital signal processing
• Natural language processing: The processing of human language by a computer program, such as spam detection and converting instantly a language to another to help humans communicate
• Robotics: A field of engineering focusing on the design and manufacturing of cyborgs, the so-called machine man. They are used to perform tasks for human's convenience or something too difficult or dangerous for human to perform and can operate without stopping such as in assembly lines
• Self-driving car: Use a combination of computer vision, image recognition amid deep learning to build automated control in a vehicle.

D O HUMAN-BEINGS REALLY NEED ARTIFICIAL INTELLIGENCE ?

Is AI really needed in human society? It depends. If human opts for a faster and effective way to complete their work and to work constantly without taking a break, yes, it is. However if humankind is satisfied with a natural way of living without excessive desires to conquer the order of nature, it is not. History tells us that human is always looking for something faster, easier, more effective, and convenient to finish the task they work on; therefore, the pressure for further development motivates humankind to look for a new and better way of doing things. Humankind as the homo-sapiens discovered that tools could facilitate many hardships for daily livings and through tools they invented, human could complete the work better, faster, smarter and more effectively. The invention to create new things becomes the incentive of human progress. We enjoy a much easier and more leisurely life today all because of the contribution of technology. The human society has been using the tools since the beginning of civilization, and human progress depends on it. The human kind living in the 21 st century did not have to work as hard as their forefathers in previous times because they have new machines to work for them. It is all good and should be all right for these AI but a warning came in early 20 th century as the human-technology kept developing that Aldous Huxley warned in his book Brave New World that human might step into a world in which we are creating a monster or a super human with the development of genetic technology.

Besides, up-to-dated AI is breaking into healthcare industry too by assisting doctors to diagnose, finding the sources of diseases, suggesting various ways of treatment performing surgery and also predicting if the illness is life-threatening [ 7 ]. A recent study by surgeons at the Children's National Medical Center in Washington successfully demonstrated surgery with an autonomous robot. The team supervised the robot to perform soft-tissue surgery, stitch together a pig's bowel, and the robot finished the job better than a human surgeon, the team claimed [ 8 , 9 ]. It demonstrates robotically-assisted surgery can overcome the limitations of pre-existing minimally-invasive surgical procedures and to enhance the capacities of surgeons performing open surgery.

Above all, we see the high-profile examples of AI including autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art, playing games (such as Chess or Go), search engines (such as Google search), online assistants (such as Siri), image recognition in photographs, spam filtering, predicting flight delays…etc. All these have made human life much easier and convenient that we are so used to them and take them for granted. AI has become indispensable, although it is not absolutely needed without it our world will be in chaos in many ways today.

T HE IMPACT OF ARTIFICIAL INTELLIGENCE ON HUMAN SOCIETY

Negative impact.

Questions have been asked: With the progressive development of AI, human labor will no longer be needed as everything can be done mechanically. Will humans become lazier and eventually degrade to the stage that we return to our primitive form of being? The process of evolution takes eons to develop, so we will not notice the backsliding of humankind. However how about if the AI becomes so powerful that it can program itself to be in charge and disobey the order given by its master, the humankind?

Let us see the negative impact the AI will have on human society [ 10 , 11 ]:

• A huge social change that disrupts the way we live in the human community will occur. Humankind has to be industrious to make their living, but with the service of AI, we can just program the machine to do a thing for us without even lifting a tool. Human closeness will be gradually diminishing as AI will replace the need for people to meet face to face for idea exchange. AI will stand in between people as the personal gathering will no longer be needed for communication
• Unemployment is the next because many works will be replaced by machinery. Today, many automobile assembly lines have been filled with machineries and robots, forcing traditional workers to lose their jobs. Even in supermarket, the store clerks will not be needed anymore as the digital device can take over human labor
• Wealth inequality will be created as the investors of AI will take up the major share of the earnings. The gap between the rich and the poor will be widened. The so-called “M” shape wealth distribution will be more obvious
• New issues surface not only in a social sense but also in AI itself as the AI being trained and learned how to operate the given task can eventually take off to the stage that human has no control, thus creating un-anticipated problems and consequences. It refers to AI's capacity after being loaded with all needed algorithm may automatically function on its own course ignoring the command given by the human controller
• The human masters who create AI may invent something that is racial bias or egocentrically oriented to harm certain people or things. For instance, the United Nations has voted to limit the spread of nucleus power in fear of its indiscriminative use to destroying humankind or targeting on certain races or region to achieve the goal of domination. AI is possible to target certain race or some programmed objects to accomplish the command of destruction by the programmers, thus creating world disaster.

P OSITIVE IMPACT

There are, however, many positive impacts on humans as well, especially in the field of healthcare. AI gives computers the capacity to learn, reason, and apply logic. Scientists, medical researchers, clinicians, mathematicians, and engineers, when working together, can design an AI that is aimed at medical diagnosis and treatments, thus offering reliable and safe systems of health-care delivery. As health professors and medical researchers endeavor to find new and efficient ways of treating diseases, not only the digital computer can assist in analyzing, robotic systems can also be created to do some delicate medical procedures with precision. Here, we see the contribution of AI to health care [ 7 , 11 ]:

Fast and accurate diagnostics

IBM's Watson computer has been used to diagnose with the fascinating result. Loading the data to the computer will instantly get AI's diagnosis. AI can also provide various ways of treatment for physicians to consider. The procedure is something like this: To load the digital results of physical examination to the computer that will consider all possibilities and automatically diagnose whether or not the patient suffers from some deficiencies and illness and even suggest various kinds of available treatment.

Socially therapeutic robots

Pets are recommended to senior citizens to ease their tension and reduce blood pressure, anxiety, loneliness, and increase social interaction. Now cyborgs have been suggested to accompany those lonely old folks, even to help do some house chores. Therapeutic robots and the socially assistive robot technology help improve the quality of life for seniors and physically challenged [ 12 ].

Reduce errors related to human fatigue

Human error at workforce is inevitable and often costly, the greater the level of fatigue, the higher the risk of errors occurring. Al technology, however, does not suffer from fatigue or emotional distraction. It saves errors and can accomplish the duty faster and more accurately.

Artificial intelligence-based surgical contribution

AI-based surgical procedures have been available for people to choose. Although this AI still needs to be operated by the health professionals, it can complete the work with less damage to the body. The da Vinci surgical system, a robotic technology allowing surgeons to perform minimally invasive procedures, is available in most of the hospitals now. These systems enable a degree of precision and accuracy far greater than the procedures done manually. The less invasive the surgery, the less trauma it will occur and less blood loss, less anxiety of the patients.

Improved radiology

The first computed tomography scanners were introduced in 1971. The first magnetic resonance imaging (MRI) scan of the human body took place in 1977. By the early 2000s, cardiac MRI, body MRI, and fetal imaging, became routine. The search continues for new algorithms to detect specific diseases as well as to analyze the results of scans [ 9 ]. All those are the contribution of the technology of AI.

Virtual presence

The virtual presence technology can enable a distant diagnosis of the diseases. The patient does not have to leave his/her bed but using a remote presence robot, doctors can check the patients without actually being there. Health professionals can move around and interact almost as effectively as if they were present. This allows specialists to assist patients who are unable to travel.

S OME CAUTIONS TO BE REMINDED

Despite all the positive promises that AI provides, human experts, however, are still essential and necessary to design, program, and operate the AI from any unpredictable error from occurring. Beth Kindig, a San Francisco-based technology analyst with more than a decade of experience in analyzing private and public technology companies, published a free newsletter indicating that although AI has a potential promise for better medical diagnosis, human experts are still needed to avoid the misclassification of unknown diseases because AI is not omnipotent to solve all problems for human kinds. There are times when AI meets an impasse, and to carry on its mission, it may just proceed indiscriminately, ending in creating more problems. Thus vigilant watch of AI's function cannot be neglected. This reminder is known as physician-in-the-loop [ 13 ].

The question of an ethical AI consequently was brought up by Elizabeth Gibney in her article published in Nature to caution any bias and possible societal harm [ 14 ]. The Neural Information processing Systems (NeurIPS) conference in Vancouver Canada in 2020 brought up the ethical controversies of the application of AI technology, such as in predictive policing or facial recognition, that due to bias algorithms can result in hurting the vulnerable population [ 14 ]. For instance, the NeurIPS can be programmed to target certain race or decree as the probable suspect of crime or trouble makers.

T HE CHALLENGE OF ARTIFICIAL INTELLIGENCE TO BIOETHICS

Artificial intelligence ethics must be developed.

Bioethics is a discipline that focuses on the relationship among living beings. Bioethics accentuates the good and the right in biospheres and can be categorized into at least three areas, the bioethics in health settings that is the relationship between physicians and patients, the bioethics in social settings that is the relationship among humankind and the bioethics in environmental settings that is the relationship between man and nature including animal ethics, land ethics, ecological ethics…etc. All these are concerned about relationships within and among natural existences.

As AI arises, human has a new challenge in terms of establishing a relationship toward something that is not natural in its own right. Bioethics normally discusses the relationship within natural existences, either humankind or his environment, that are parts of natural phenomena. But now men have to deal with something that is human-made, artificial and unnatural, namely AI. Human has created many things yet never has human had to think of how to ethically relate to his own creation. AI by itself is without feeling or personality. AI engineers have realized the importance of giving the AI ability to discern so that it will avoid any deviated activities causing unintended harm. From this perspective, we understand that AI can have a negative impact on humans and society; thus, a bioethics of AI becomes important to make sure that AI will not take off on its own by deviating from its originally designated purpose.

Stephen Hawking warned early in 2014 that the development of full AI could spell the end of the human race. He said that once humans develop AI, it may take off on its own and redesign itself at an ever-increasing rate [ 15 ]. Humans, who are limited by slow biological evolution, could not compete and would be superseded. In his book Superintelligence, Nick Bostrom gives an argument that AI will pose a threat to humankind. He argues that sufficiently intelligent AI can exhibit convergent behavior such as acquiring resources or protecting itself from being shut down, and it might harm humanity [ 16 ].

The question is–do we have to think of bioethics for the human's own created product that bears no bio-vitality? Can a machine have a mind, consciousness, and mental state in exactly the same sense that human beings do? Can a machine be sentient and thus deserve certain rights? Can a machine intentionally cause harm? Regulations must be contemplated as a bioethical mandate for AI production.

Studies have shown that AI can reflect the very prejudices humans have tried to overcome. As AI becomes “truly ubiquitous,” it has a tremendous potential to positively impact all manner of life, from industry to employment to health care and even security. Addressing the risks associated with the technology, Janosch Delcker, Politico Europe's AI correspondent, said: “I don't think AI will ever be free of bias, at least not as long as we stick to machine learning as we know it today,”…. “What's crucially important, I believe, is to recognize that those biases exist and that policymakers try to mitigate them” [ 17 ]. The High-Level Expert Group on AI of the European Union presented Ethics Guidelines for Trustworthy AI in 2019 that suggested AI systems must be accountable, explainable, and unbiased. Three emphases are given:

• Lawful-respecting all applicable laws and regulations
• Ethical-respecting ethical principles and values
• Robust-being adaptive, reliable, fair, and trustworthy from a technical perspective while taking into account its social environment [ 18 ].

Seven requirements are recommended [ 18 ]:

• AI should not trample on human autonomy. People should not be manipulated or coerced by AI systems, and humans should be able to intervene or oversee every decision that the software makes
• AI should be secure and accurate. It should not be easily compromised by external attacks, and it should be reasonably reliable
• Personal data collected by AI systems should be secure and private. It should not be accessible to just anyone, and it should not be easily stolen
• Data and algorithms used to create an AI system should be accessible, and the decisions made by the software should be “understood and traced by human beings.” In other words, operators should be able to explain the decisions their AI systems make
• Services provided by AI should be available to all, regardless of age, gender, race, or other characteristics. Similarly, systems should not be biased along these lines
• AI systems should be sustainable (i.e., they should be ecologically responsible) and “enhance positive social change”
• AI systems should be auditable and covered by existing protections for corporate whistleblowers. The negative impacts of systems should be acknowledged and reported in advance.

From these guidelines, we can suggest that future AI must be equipped with human sensibility or “AI humanities.” To accomplish this, AI researchers, manufacturers, and all industries must bear in mind that technology is to serve not to manipulate humans and his society. Bostrom and Judkowsky listed responsibility, transparency, auditability, incorruptibility, and predictability [ 19 ] as criteria for the computerized society to think about.

S UGGESTED PRINCIPLES FOR ARTIFICIAL INTELLIGENCE BIOETHICS

Nathan Strout, a reporter at Space and Intelligence System at Easter University, USA, reported just recently that the intelligence community is developing its own AI ethics. The Pentagon made announced in February 2020 that it is in the process of adopting principles for using AI as the guidelines for the department to follow while developing new AI tools and AI-enabled technologies. Ben Huebner, chief of the Office of Director of National Intelligence's Civil Liberties, Privacy, and Transparency Office, said that “We're going to need to ensure that we have transparency and accountability in these structures as we use them. They have to be secure and resilient” [ 20 ]. Two themes have been suggested for the AI community to think more about: Explainability and interpretability. Explainability is the concept of understanding how the analytic works, while interpretability is being able to understand a particular result produced by an analytic [ 20 ].

All the principles suggested by scholars for AI bioethics are well-brought-up. I gather from different bioethical principles in all the related fields of bioethics to suggest four principles here for consideration to guide the future development of the AI technology. We however must bear in mind that the main attention should still be placed on human because AI after all has been designed and manufactured by human. AI proceeds to its work according to its algorithm. AI itself cannot empathize nor have the ability to discern good from evil and may commit mistakes in processes. All the ethical quality of AI depends on the human designers; therefore, it is an AI bioethics and at the same time, a trans-bioethics that abridge human and material worlds. Here are the principles:

• Beneficence: Beneficence means doing good, and here it refers to the purpose and functions of AI should benefit the whole human life, society and universe. Any AI that will perform any destructive work on bio-universe, including all life forms, must be avoided and forbidden. The AI scientists must understand that reason of developing this technology has no other purpose but to benefit human society as a whole not for any individual personal gain. It should be altruistic, not egocentric in nature
• Value-upholding: This refers to AI's congruence to social values, in other words, universal values that govern the order of the natural world must be observed. AI cannot elevate to the height above social and moral norms and must be bias-free. The scientific and technological developments must be for the enhancement of human well-being that is the chief value AI must hold dearly as it progresses further
• Lucidity: AI must be transparent without hiding any secret agenda. It has to be easily comprehensible, detectable, incorruptible, and perceivable. AI technology should be made available for public auditing, testing and review, and subject to accountability standards … In high-stakes settings like diagnosing cancer from radiologic images, an algorithm that can't “explain its work” may pose an unacceptable risk. Thus, explainability and interpretability are absolutely required
• Accountability: AI designers and developers must bear in mind they carry a heavy responsibility on their shoulders of the outcome and impact of AI on whole human society and the universe. They must be accountable for whatever they manufacture and create.

C ONCLUSION

AI is here to stay in our world and we must try to enforce the AI bioethics of beneficence, value upholding, lucidity and accountability. Since AI is without a soul as it is, its bioethics must be transcendental to bridge the shortcoming of AI's inability to empathize. AI is a reality of the world. We must take note of what Joseph Weizenbaum, a pioneer of AI, said that we must not let computers make important decisions for us because AI as a machine will never possess human qualities such as compassion and wisdom to morally discern and judge [ 10 ]. Bioethics is not a matter of calculation but a process of conscientization. Although AI designers can up-load all information, data, and programmed to AI to function as a human being, it is still a machine and a tool. AI will always remain as AI without having authentic human feelings and the capacity to commiserate. Therefore, AI technology must be progressed with extreme caution. As Von der Leyen said in White Paper on AI – A European approach to excellence and trust : “AI must serve people, and therefore, AI must always comply with people's rights…. High-risk AI. That potentially interferes with people's rights has to be tested and certified before it reaches our single market” [ 21 ].

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Conflicts of interest.

There are no conflicts of interest.

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