18.7 Ethics

Moral and ethical issues abound in considering the impacts of AI. Morals are guidelines that apply to an individual’s sense of right and wrong. Ethical principles apply at the level of a community, an organization, or a profession. Morals and ethics are, of course, intimately connected: an individual may have personal morals that derive from various sources, including the ethical principles of groups they belong to. Normative ethical codes are categorized, by philosophers, as either virtue-based, consequentialist, or deontological [Hursthouse and Pettigrove, 2018]:

  • Virtue ethics emphasize the values and character traits that a virtuous agent possesses [Vallor, 2021].

  • Consequentialist (or utilitarian) ethics focus on the outcomes of possible actions that the agent can take, measuring the global utility of each outcome.

  • Deontological (or Kantian) ethical codes are based on a set of rules the agent should follow.

A focus on AI ethics has arisen, motivated, in part, by worries about whether AI systems can be expected to behave properly. Reliance on autonomous intelligent agents raises the question: can we trust AI systems? They are not fully trustworthy and reliable, given the way they are built now. So, can they do the right thing? Will they do the right thing? But trust is not just about the system doing the right thing. A human will only see a system as trustworthy if they are confident that it will reliably do the right thing. As evidenced by popular movies and books, in our collective unconscious, the fear exists that robots, and other AI systems, are untrustworthy. They may become completely autonomous, with free will, intelligence, and consciousness. They may rebel against us as Frankenstein-like monsters.

Issues of trust raise questions about ethics. If the designers, implementers, deployers, and users of AI systems are following explicit ethical codes, those systems are more likely to be trusted. Moreover, if those systems actually embody explicit ethical codes, they are also more likely to be trusted. The discipline of AI ethics is concerned with answering questions such as:

  • Should AI scientists be guided by ethical principles in developing theories, algorithms, and tools?

  • What are ethical activities for designers and developers of AI systems?

  • For deployers of AI systems, are there applications that should not be considered?

  • Should humans be guided by ethical principles when interacting with AI systems?

  • Should AI systems be guided by ethical principles, in their interactions with humans, other agents, or the rest of the world?

  • What data should be used to train AI systems?

  • For each of these concerns, who determines the ethical codes that apply?

AI ethics, as an emerging and evolving discipline, addresses two, distinct but related, issues:

  1. A.

    AI ethics for humans: researchers, designers, developers, deployers, and users.

  2. B.

    AI ethics for systems: software agents and embodied robots.

Each is concerned with developing and examining ethical codes, of one of the three code types, either for humans or for systems.

With regard to AI ethics for humans, many perceive a need for strong professional codes of ethics for AI designers and engineers, just as there are for engineers in all other disciplines. Others disagree. The legal, medical, and computing professions all have explicit deontological ethics codes that practitioners are expected or required to follow. For computing, the ACM Committee on Professional Ethics [2018], AAAI [2019], and IEEE [2020] have established ethics codes that apply to their members.

There are several issues around what should be done ethically in designing, building, and deploying AI systems. What ethical issues arise for us, as humans, as we interact with them? Should we give them any rights? There are human rights codes; will there be AI systems rights codes, as well?

Philosophers distinguish among moral agents, moral patients, and other agents. Moral agents can tell right from wrong and can be held responsible for their actions. A moral patient is an agent who should be treated with moral principles by a moral agent. So, for example, a typical adult human is a moral agent, and a moral patient; a baby is a moral patient but not a moral agent, whereas a (traditional) car is neither. There is an ongoing debate as to whether an AI agent could ever be (or should ever be) a moral agent. Moreover, should current AI systems be considered as moral patients, warranting careful ethical treatment by humans? Presumably not, but is it conceivable that future AI systems, including robots, could be, or should be, ever treated as moral patients? Some of the underlying issues are covered by Bryson [2011], Mackworth [2011], Bryson [2018], Gunkel [2018], and Nyholm [2021]. These issues are partially addressed by the multitude of proposed codes of AI ethics such as those developed by the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems [2019], OECD [2019], and UNESCO [2022].


Facial Recognition

Selinger and Leong [2021], studying the ethics of facial recognition technology, define four forms of facial recognition, each with their own risks and benefits:

  • facial detection finds the location of faces in images, it is common in phones, putting square overlays on faces

  • facial characterization finds features of individual faces, such as approximate age, emotions (e.g., smiling or sad), and what the person is looking at

  • facial verification determines whether the person matches a single template; it is used to verify the user of a phone and in airport security

  • facial identification is used to identify each person in an image from a database of faces; it is used in common photo library software to identify friends and family members.

Facial identification, usually considered the most problematic, has problems that arise both when it is perfect and when it makes mistakes.

If facial identification is perfect and pervasive, people will know they are constantly under surveillance. This means they will be very careful to not do anything that is illegal or anything out of narrow social norms. People’s behavior is self-censored, even if they have no intention to commit any wrongdoing. Preventing illegal activity becomes problematic when any criticism of the ruling order or anything that deviates from a narrow definition of normal behavior becomes illegal. Surveillance has a chilling effect that limits self-expression, creativity, and growth, and deprives the marketplace of ideas.

When facial identification makes mistakes, they usually do not affect all groups equally. The error rate is usually much worse for socially disadvantaged people, which can result in those people becoming more targeted.

Given a database of faces, facial identification becomes a combination of facial detection and facial verification. The facial verification on an iPhone uses multiple sensors to build a three-dimensional model of a face based on 30,000 points. It has privacy-by-design; the information is stored locally on the phone and not in a server. It has a false-positive rate of 1 in 10 million, which means it is unlikely to have a false positive in normal use. If the same error rate was used on a database of everyone, on average there are about 800 people on Earth who match a particular face. Vision-only techniques have much higher error rates, which would mean that mis-identification would be very common.

People have argued that making facial recognition, in any of its forms, part of normal life provides a slippery slope where it is easy to slip into problematic cases. For example, if a community already has surveillance cameras to detect and prevent crime, it can be very cheap to get extra value out of the cameras by adding on facial recognition.


With regard to AI ethics for systems, how should AI systems make decisions as they develop more autonomy? Consider some interesting, if perhaps naive, proposals put forward by the science fiction novelist Isaac Asimov [1950], one of the earliest thinkers about these issues. Asimov’s Laws of Robotics are a good basis to start from because, at first glance, they seem logical and succinct.

Asimov’s original three laws are:

  1. I.

    A robot may not harm a human being, or, through inaction, allow a human being to come to harm.

  2. II.

    A robot must obey the orders given to it by human beings except where such orders would conflict with the First Law.

  3. III.

    A robot must protect its own existence, as long as such protection does not conflict with the First or Second Laws.

Asimov proposed those prioritized laws should be followed by all robots and, by statute, manufacturers would have to guarantee that. The laws constitute a deontological code of ethics for robots, imposing constraints on acceptable robotic behavior. Asimov’s plots arise mainly from the conflict between what the humans intend the robot to do and what it actually does, or between literal and sensible interpretations of the laws, because they are not codified in any formal language. Asimov’s fiction explored many hidden implicit contradictions in the laws and their consequences.

There are ongoing discussions of AI ethics for systems, but the discussions often presuppose technical abilities to impose and verify AI system safety requirements that just do not exist yet. Some progress on formal hardware and software verification is described in Section 5.10. Joy [2000] was so concerned about our inability to control the dangers of new technologies that he called, unsuccessfully, for a moratorium on the development of robotics (and AI), nanotechnology, and genetic engineering.

Perhaps the intelligent agent design space and the agent design principles developed in this book could provide a more technically informed framework for the development of social, ethical, and legal codes for intelligent agents.

However, in skeptical opposition, Munn [2022] argues that “AI ethical principles are useless, failing to mitigate the racial, social, and environmental damages of AI technologies in any meaningful sense.” But see also “In defense of ethical guidelines” by Lundgren [2023].