Ethical and moral issues of artificial intelligence

From to there have been more than films produced worldwide about artificial intelligence. And while some scenarios are depicted in a good light, the rest are downright horrific. In movies such as The TerminatorThe MatrixAvengers: Age of Ultron and many others, the movie industry placed into our shared imagination scenes demonstrating how more intelligent machines will take over the world and enslave or totally wipe humanity from existence.

The potential for AIs to become more superior than any human intelligence paints a dark future for humanity. Artificial intelligence is red hot. But what ethical and practical issues should we consider while moving full-steam ahead in embracing AI technology? In our shared goal to transform business sectors using machine intelligence, what risks and responsibilities should innovators consider?

Yes, AI agents will be — and already are — very capable of completing processes parallel to human intelligence. Universities, private organizations and governments are actively developing artificial intelligence with the ability to mimic human cognitive functions such as learning, problem-solving, planning and speech recognition. But if these agents lack empathy, instinct and wisdom in decision-making, should their integration into society be limited, and if so, in what ways?

By way of disclaimer, this article is by no means meant to persuade your opinion, but merely to highlight some of the salient issues, both large and small. While Kambria is a supporter of AI and robotics technology, we are by no means ethics experts and leave it up to decide where you stand.

A robot vacuum is one thing, but ethical questions around AI in medicine, law enforcement, military defense, data privacy, quantum computing, and other areas are profound and important to consider.

One of the primary concerns people have with AI is future loss of jobs. Should we strive to fully develop and integrate AI into society if it means many people will lose their jobs — and quite possibly their livelihood?

According to the new McKinsey Global Institute reportby the yearabout million people will lose their jobs to AI-driven robots.

Is Developing Artificial Intelligence (AI) Ethical? - Idea Channel - PBS Digital Studios

Some would argue that if their jobs are taken by robots, perhaps they are too menial for humans and that AI can be responsible for creating better jobs that take advantage of unique human ability involving higher cognitive functions, analysis and synthesis.

Another point is that AI may create more jobs — after all, people will be tasked with creating these robots to begin with and then manage them in the future. One issue related to job loss is wealth inequality. Consider that most modern economic systems require workers to produce a product or service with their compensation based on an hourly wage.Ethics and law are inextricably linked in modern society, and many legal decisions arise from the interpretation of various ethical issues.

Artificial intelligence adds a new dimension to these questions. Systems that use artificial intelligence technologies are becoming increasingly autonomous in terms of the complexity of the tasks they can perform, their potential impact on the world and the diminishing ability of humans to understand, predict and control their functioning.

Most people underestimate the real level of automation of these systems, which have the ability to learn from their own experience and perform actions beyond the scope of those intended by their creators. This causes a number of ethical and legal difficulties that we will touch upon in this article. There is a well-known thought experiment in ethics called the trolley problem.

The experiment raises a number of important ethical issues that are directly related to artificial intelligence. Imagine a runaway trolley going down the railway lines. There are five people tied to the track ahead.

You are standing next to a lever. If you pull it, the trolley will switch to a different set of track. However, there is another person tied to that set of track. Do you pull the lever or not? There is no clear-cut answer to this question. What is more, there are numerous situations in which such a decision may have to be made [1].

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And different social groups tend to give different answers. For example, Buddhist monks are overwhelmingly willing to sacrifice the life of one person in order to save five, even if presented with a more complicated variation of the trolley problem. As for artificial intelligence, such a situation could arise, for example, if a self-driving vehicle is travelling along a road in a situation where an accident is unavoidable.

The question thus arises as to whose lives should take priority — those of the passengers, the pedestrians or neither. A special website has been created by the Massachusetts Institute of Technology that deals with this very issue: users can test out various scenarios out on themselves and decide which courses of action would be the most worthwhile.

Other questions also arise in this case: What actions can be allowed from the legal point of view? What should serve as a basis for such decisions? Who should ultimately be held responsible?

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This problem has already been addressed by companies and regulators. Representatives at Mercedes, for example, have said outright that their cars will prioritize the lives of passengers.

The Federal Ministry of Transport and Digital Infrastructure of Germany responded to this immediately, anticipating future regulation by stating that making such a choice based on a set of criteria would be illegal, and that the car manufacturer be held responsible for any injury or loss of life. Other countries may go a different route. Take the Chinese Social Credit Systemfor example, which rates its citizens based how law-abiding and how useful to society they are, etc.

Those with low ratings will face sanctions. What is stopping the Chinese government from introducing a law that forces manufacturers of self-driving vehicles to sacrifice the lives of lower-rated citizens in the event of an unavoidable accident? Face recognition technologies and access to the relevant databases make it perfectly possible to identify potential victims and compare their social credit ratings. The legal problems run even deeper, especially in the case of robots.

A system that learns from information it receives from the outside world can act in ways that its creators could not have predicted [2], and predictability is crucial to modern legal approaches. What is more, such systems can operate independently from their creators or operators thus complicating the task of determining responsibility.

These characteristics pose problems related to predictability and the ability to act independently while at the same time not being held responsible [3].

There are numerous options in terms of regulation, including regulation that is based on existing norms and standards.Views are his own.

Artificial intelligence and machine learning technologies are rapidly transforming society and will almost certainly continue to do so in the coming decades. This social transformation will have deep ethical impact, with these powerful new technologies both improving and disrupting human lives.

ethical and moral issues of artificial intelligence

AI, as the externalization of human intelligence, offers us in amplified form everything that humanity already is, both good and evil. Much is at stake. At this crossroads in history we should think very carefully about how to make this transition, or we risk empowering the grimmer side of our nature, rather than the brighter. In that spirit, we offer a preliminary list of issues with ethical relevance in AI and machine learning. The first question for any technology is whether it works as intended.

Will AI systems work as they are promised or will they fail? If and when they fail, what will be the results of those failures? And if we are dependent upon them, will we be able to survive without them? The question of technical safety and failure is separate from the question of how a properly-functioning technology might be used for good or for evil questions 3 and 4, below. This question is merely one of function, yet it is the foundation upon which all the rest of the analysis must build.

Once we have determined that the technology functions adequately, can we actually understand how it works and properly gather data on its functioning? Ethical analysis always depends on getting the facts first — only then can evaluation begin. It turns out that with some machine learning techniques such as deep learning in neural networks it can be difficult or impossible to really understand why the machine is making the choices that it makes. In other cases, it might be that the machine can explain something, but the explanation is too complex for humans to understand.

Explanations of this sort might be true explanations, but humans will never know for sure. As an additional point, in general, the more powerful someone or something is, the more transparent it ought to be, while the weaker someone is, the more right to privacy he or she should have. Therefore the idea that powerful AIs might be intrinsically opaque is disconcerting.

A perfectly well functioning technology, such as a nuclear weapon, can, when put to its intended use, cause immense evil. Artificial intelligence, like human intelligence, will be used maliciously, there is no doubt. For example, AI-powered surveillance is already widespread, in both appropriate contexts e. More obviously nefarious examples might include AI-assisted computer-hacking or lethal autonomous weapons systems LAWSa.

While movies and weapons technologies might seem to be extreme examples of how AI might empower evil, we should remember that competition and war are always primary drivers of technological advance, and that militaries and corporations are working on these technologies right now. History also shows that great evils are not always completely intended e.

Because of this, forbidding, banning, and relinquishing certain types of technology would be the most prudent solution. The main purpose of AI is, like every other technology, to help people lead longer, more flourishing, more fulfilling lives.

This is good, and therefore insofar as AI helps people in these ways, we can be glad and appreciate the benefits it gives to us. Additional intelligence will likely provide improvements in nearly every field of human endeavor, including, for example, archaeology, biomedical research, communication, data analytics, education, energy efficiency, environmental protection, farming, finance, legal services, medical diagnostics, resource management, space exploration, transportation, waste management, and so on.

As just one concrete example of a benefit from AI, some farm equipment now has computer systems capable of visually identifying weeds and spraying them with tiny targeted doses of herbicide. This not only protects the environment by reducing the use of chemicals on crops, but it also protects human health by reducing exposure to these chemicals.

One of the interesting things about neural networks, the current workhorses of artificial intelligence, is that they effectively merge a computer program with the data that is given to it. This has many benefits, but it also risks biasing the entire system in unexpected and potentially detrimental ways.As you read Lowenthal's chapter 13 that discusses "Ethical and Moral Issues in Intelligence," do some critical thinking and ask yourself:. As you read through this section, consider what other general moral questions a different author with a different background might have asked, or how they might have addressed these issues differently.

Reference the first bullet above, and read the news release again on the Penn State Graduate Certificate in Geospatial Intelligence. Does the press release in any way relate to the discussion of ethics and "good" decision making? What does it tell you about the concerns of the Penn State faculty that had to approve the program?

University Park, Pa. The five-course, credit post baccalaureate program is designed to provide students with the core competencies required to effectively and ethically provide geospatial analysis to key decision makers at defense, governmental, business and nongovernmental organizations. Geospatial intelligence is a combination of remote sensing, imagery capture, geographic surveying and geo-political analysis. Its uses vary widely and can be applied to military planning, environmental resource preservation and even strategic retail store placement.

Since a call to significantly increase the number of geospatial analysts in the government, the demand for qualified individuals has far outpaced the development of newly qualified professionals. There is "a critical need" for this kind of educational offering, according to K.

Ethical and moral issues regarding AI

Stuart Shea, president and chairman of the U. Where do you place your resources? How are events on the Earth related? Rather than simply developing students' proficiency with technology, Penn State's geography faculty want to develop students' abilities in critical thinking and spatial analysis, while promoting cultural sensitivity and high ethical standards to students in the field.

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The capstone course for the program is a virtual field experience. It will require students to problem solve a crisis situation modeled after real-world experiences — complete with unexpected curve balls thrown in by the instructors. Penn State's Geospatial Intelligence Certificate program is the first online program of its kind in the nation. The certificate requires less than two years to complete, and more information is available at this link: Graduate Certificate in Geospatial Intelligence.

We know from earlier readings that one of the mortal sins in the intelligence business is to politicize intelligence. Fifteen years ago, the Senate Select Committee on Intelligence asked me to testify at the confirmation hearings for Robert M.

ethical and moral issues of artificial intelligence

Gates, who had been nominated to be director of Central Intelligence. I was asked because I had worked in the CIA's office of Soviet analysis back when Gates was the agency's deputy director for intelligence and chairman of the National Intelligence Council. More specifically, I was asked to testify because of my knowledge about the creation of a May special National Intelligence Estimate on Iran that had been used to justify the ill-fated deals known as Iran-Contra.

It seems like a long time ago now. Iran-Contra is just one of many scandals that have come and gone in the intervening years. But today, in the aftermath of the U. Induring Ronald Reagan's second term as president, the U. Casey were known for their aggressive anti-Soviet rhetoric and policies. Gates, as Casey's deputy, shared their ideology. Iran-Contra was in the planning stages then, a secret scheme in which the Reagan administration was going to sell arms to an enemy country, Iran, and use the proceeds to fund the anti-communist Contras in Nicaragua.

In order to justify these actions, administration officials felt they needed some analytical backing from the intelligence community. Those in my office knew nothing of their plans, of course, but it was the context in which we were asked, into contribute to the National Intelligence Estimate on the subject of Iran.

Later, when we received the draft NIE, we were shocked to find that our contribution on Soviet relations with Iran had been completely reversed. Rather than stating that the prospects for improved Soviet-Iranian relations were negligible, the document indicated that Moscow assessed those prospects as quite good.

What's more, the national intelligence officer responsible for coordinating the estimate had already sent a personal memo to the White House stating that the race between the U. No one in my office believed this Cold War hyperbole. There was simply no evidence to support the notion that Moscow was optimistic about its prospects for improved relations with Iran.Most aspects of our lives are now touched by artificial intelligence in one way or another, from deciding what books or flights to buy online to whether our job applications are successful, whether we receive a bank loan, and even what treatment we receive for cancer.

All of these things — and many more — can now be determined largely automatically by complex software systems. The enormous strides AI has made in the last few years are striking — and AI has the potential to make our lives better in many ways. In the last couple of years, the rise of artificial intelligence has been inescapable. Vast sums of money have been thrown at AI start-ups. Many existing tech companies — including the giants like Amazon, Facebook, and Microsoft - have opened new research labs.

Artificial intelligence has proved itself in many practical tasks - from labeling photos to diagnosing disease Credit: Getty Images. Some predict an upheaval as big as — or bigger — than that brought by the internet. We asked a panel of technologists what this rapidly changing world brimming with brilliant machines has in store for humans. Remarkably, nearly all of their responses centre on the question of ethics.

For Peter Norvig, director of research at Google and a pioneer of machine learning, the data-driven AI technique behind so many of its recent successes, the key issue is working out how to ensure that these new systems improve society as a whole — and not just those who control it. The big problem is that the complexity of the software often means that it is impossible to work out exactly why an AI system does what it does.

So we take it on trust. The challenge then is to come up with new ways of monitoring or auditing the very many areas in which AI now plays such a big role. For Jonathan Zittrain, a professor of internet law at Harvard Law School, there is a danger that the increasing complexity of computer systems might prevent them from getting the scrutiny they need.

Artificial intelligence will let robots do more complicated jobs, such as this shop assistant serving customers in Japan Credit: Getty Images. AI will need oversight, but it is not yet clear how that should be done.

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Yet in a fast-moving world, regulatory bodies often find themselves playing catch up. In many crucial areas, such as the criminal justice system and healthcare, companies are already exploring the effectiveness of using artificial intelligence to make decisions about parole or diagnose disease.

Danah Boyd, principle researcher at Microsoft Research, says there are serious questions about the values that are being written into such systems — and who is ultimately responsible for them. One area fraught with ethical issues is the workplace. AI will let robots do more complicated jobs and displace more human workers. In many factories, humans already work alongside robots - some think feelings of displacement could have knock-on effects on mental health Credit: Getty Images.I n the race to adopt rapidly developing technologies, organisations run the risk of overlooking potential ethical implications.

And that could produce unwelcome results, especially in artificial intelligence AI systems that employ machine learning. Machine learning is a subset of AI in which computer systems are taught to learn on their own. Algorithms allow the computer to analyse data to detect patterns and gain knowledge or abilities without having to be specifically programmed.

It is this type of technology that empowers voice-enabled assistants such as Apple's Siri or the Google Assistant, among myriad other uses. In the accounting space, the many potential applications of AI include real-time auditing and analysis of company financials.

Data is the fuel that powers machine learning.

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But what happens if the data fed to the machine are flawed or the algorithm that guides the learning isn't properly configured to assess the data it's receiving? Things could go very wrong remarkably quickly. Microsoft learned this lesson in when the company designed a chatbot called Tay to interact with Twitter users. A group of those users took advantage of a flaw in Tay's algorithm to corrupt it with racist and otherwise offensive ideas.

Within 24 hours of launch, the chatbot had said the Holocaust was "made up", expressed support for genocide, and had to be taken offline. With regulatory and legal frameworks struggling to keep pace with the rapid pace of technological change, public demand is growing for greater transparency as to how these tools and technologies are being used.

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The UK's Institute of Business Ethics IBE recently issued a briefing urging organisations to examine the risks, impacts, and side effects that AI might have for their business and their stakeholders, as well as wider society.

Tackling the issues requires these diverse groups to work together. The research identifies a number of challenges facing business leaders. These include:. The report also encourages companies to "improve their communications around AI, so that people feel that they are part of its development and not its passive recipients or even victims". For this to be achieved, "[e]mployees and other stakeholders need to be empowered to take personal responsibility for the consequences of their use of AI, and they need to be provided with the skills to do so".

The report proposes a framework outlining ten core values and principles for the use of AI in business. These are intended to "minimise the risk of ethical lapses due to an improper use of AI technologies". The values are:. Companies applying AI to the finance function face the challenge of designing algorithms that produce unbiased results and are not too complex for users to understand how they work and make decisions.

The product uses a hybrid of advanced algorithmic techniques to enhance a human auditor's ability to detect and address unusual financial circumstances. A key aspect of the MindBridge application is that it explains why certain transactions have been highlighted and then leaves final decision-making authority to a human, said chief technology officer Robin Grosset.

This transparency is essential to avoid the "black box" problem, in which a computer or other system produces results but provides little to no explanation for how those results were produced. In the case of machine learning, the greater the complexity of an algorithm, the more difficult it is for users to understand why the machine has made a certain decision.

Human judgement is still a key component of a balanced AI system. Another challenge is to avoid bias in the algorithm and in the dataset the algorithm uses for learning. One way of mitigating bias is to use combinations of learning types, including unsupervised learning, Grosset said. By contrast, unsupervised learning has no labels and essentially will find what is in the data without any bias. For example, if you are creating an AI to automate driving a car, you want your AI to learn from good drivers and not from bad drivers," Grosset said.

MindBridge's testing process includes validation testing for algorithm intent, with regression testing. The process involves synthetic and real data.In a perspective piece, Stanford researchers discuss the ethical implications of using machine-learning tools in making health care decisions for patients. Artificial intelligence is hard at work crunching health data to improve diagnostics and help doctors make better decisions for their patients.

But researchers at the Stanford University School of Medicine say the furious pace of growth in the development of machine-learning tools calls for physicians and scientists to carefully examine the ethical risks of incorporating them into decision-making.

In a perspective piece published March 15 in The New England Journal of Medicinethe authors acknowledged the tremendous benefit that machine learning can have on patient health. David MagnusPhD, senior author of the piece and director of the Stanford Center for Biomedical Ethicssaid bias can play into health data in three ways: human bias; bias that is introduced by design; and bias in the ways health care systems use the data.

The Ethical and Legal Issues of Artificial Intelligence

Raffin Professor of Medicine and Biomedical Ethics. What if different treatment decisions about patients are made depending on insurance status or their ability to pay? They also put the responsibility for finding solutions and setting the agenda on health care professionals. Remaining ignorant about the construction of machine-learning systems or allowing them to be constructed as black boxes could lead to ethically problematic outcomes. The authors acknowledge the social pressure to incorporate the latest tools in order to provide better health outcomes for patients.

ethical and moral issues of artificial intelligence

But health care systems need to be aware of the pitfalls that have happened in other industries, he added. Shah noted that models are only as trustworthy as the data being gathered and shared. The authors wrote that what physicians learn from the data needs to be heavily weighed against what they know from their own clinical experience.

Overreliance on machine guidance might lead to self-fulfilling prophesies. For example, they said, if clinicians always withdraw care in patients with certain diagnoses, such as extreme prematurity or brain injury, machine-learning systems may learn that such diagnoses are always fatal.

Conversely, machine-learning systems, properly deployed, may help resolve disparities in health care delivery by compensating for known biases or by identifying where more research is needed to balance the underlying data. Magnus said the example of a current pilot study of an algorithm developed at Stanford to predict the need for a palliative care consultation illustrates how collaborative, careful consideration in the design of an algorithm and use of the data can guard against the misinterpretation of data in making care decisions.

Shah is helping to lead the pilot study. Magnus said the pressure to turn to data for answers is especially intense in fields that are growing quickly, such as genetic testing and sequencing. A dedicated page provides the latest information and developments related to the pandemic. Stanford Medicine is leading the biomedical revolution in precision health, defining and developing the next generation of care that is proactive, predictive and precise.

Learn more. Researchers say use of artificial intelligence in medicine raises ethical questions. Mar 14 Physicians must adequately understand how algorithms are created, critically assess the source of the data used to create the statistical models designed to predict outcomes, understand how the models function and guard against becoming overly dependent on them.

Could data become the doctor? Press Releases. Email her at phannon stanford. Leading In Precision Health.

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