AI ethics
In plain wordsThe study of what is right and wrong in how AI is built and used: who it may harm, who is responsible when it fails, and which values it should respect.
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The branch of applied ethics, and the associated professional practice, concerned with the moral questions raised by the design, deployment and governance of AI systems: biasalgorithmic bias, n.: Unfairness in software that judges or serves people: it consistently produces worse results for some groups, such as women, older people or ethnic minorities, often because it learned from skewed or incomplete records. and fairnessalgorithmic fairness, n.: The effort to make automated decisions fair: to say in numbers what fair treatment means, measure whether a system achieves it, and correct the system if it does not., privacy and surveillanceAI surveillance, n.: Using AI to watch, identify, track or profile people on a scale no team of humans could manage, for example by scanning camera feeds or analyzing phone and online data., transparencyAI transparency, n.: Openness about AI: telling people when they are dealing with one, how a system was built and tested, and why it reached a particular decision. and explainabilityexplainability, n.: How well the reasons behind an AI system's outputs can be put into terms that people can understand, check and, if needed, challenge., accountability and the responsibility gapresponsibility gap, n.: The problem that when an AI system that learns or acts on its own causes harm, it may be unclear whether anyone (its makers, users or operators) can fairly be held responsible., autonomy and manipulation, effects on work, environmental costs and the distribution of power. In practice it ranges from academic philosophy to corporate principles, review boards, auditsAI audit, n.: An independent check of an AI system, or of the company using it, against agreed standards — for example, testing whether a hiring tool treats women and men fairly. and regulation. Reviewing 84 guideline documents in 2019, Anna Jobin, Marcello Ienca and Effy Vayena found five principles recurring in more than half of them (transparency; justice and fairness; non-maleficence; responsibility; privacy), with little agreement on what each requires in practice.
The AI ethics review flagged that the triage model had never been tested on children.
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Machine ethics
Less commonly, the project of giving AI systems the capacity to reason about and act on moral considerations themselves, so that they behave as ethical agents; more usually called machine ethicsmachine ethics, n.: The effort to build AI systems that can tell right from wrong and act ethically themselves, rather than only being used ethically by people..
Origin
Concern with the moral effects of thinking machines goes back to Norbert Wiener's The Human Use of Human Beings (1950) and Joseph Weizenbaum's Computer Power and Human Reason (1976). Computer ethics became a field in the 1980s; 'AI ethics' spread as a label in the mid-2010s, followed by a wave of principles documents from companies, governments and civil society in 2016–2019.
Usage note
Often contrasted with AI safetyAI safety, n.: The effort to keep AI from doing serious harm, whether because it pursues aims its makers never intended, is turned to bad uses or simply malfunctions — a concern that grows as systems become more capable.: ethics work has tended to focus on present, documented harms to people and groups, safety work on failures of increasingly capable systems, including catastrophic ones. The communities overlap and the divide is increasingly questioned. Critics warn that voluntary principles without enforcement can become ethics washingethics washing, n.: Using talk of ethics, such as principles, advisory boards or pledges, to look responsible and head off regulation, without actually changing what a company does..
Related
- responsible AIresponsible AI, n.: An umbrella name for the practices organizations use to keep their AI systems fair, safe, private and accountable, from the first design meeting to everyday use.
- trustworthy AItrustworthy AI, n.: AI that deserves people's trust because it works reliably, obeys the law, treats people fairly and can be checked, rather than AI that people merely happen to trust.
- algorithmic fairnessalgorithmic fairness, n.: The effort to make automated decisions fair: to say in numbers what fair treatment means, measure whether a system achieves it, and correct the system if it does not.
- AI transparencyAI transparency, n.: Openness about AI: telling people when they are dealing with one, how a system was built and tested, and why it reached a particular decision.
- value-sensitive designvalue-sensitive design, n.: A way of designing technology that deliberately identifies the human values at stake, such as privacy, fairness or autonomy, and builds them in from the start instead of patching problems later.
- machine ethicsmachine ethics, n.: The effort to build AI systems that can tell right from wrong and act ethically themselves, rather than only being used ethically by people.
- AI governanceAI governance, n.: The rules, institutions and habits that shape how AI is built and used — from laws and treaties to technical standards and a company's own review processes.
- UNESCO Recommendation on the Ethics of AIUNESCO Recommendation on the Ethics of AI, prop. n.: A worldwide set of ethical principles for AI that all of UNESCO's member countries agreed in 2021, covering human rights, fairness, privacy, the environment, gender equality and education.
- AI safetyAI safety, n.: The effort to keep AI from doing serious harm, whether because it pursues aims its makers never intended, is turned to bad uses or simply malfunctions — a concern that grows as systems become more capable.
Sources
- Jobin, A., Ienca, M. & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence 1, 389–399.
- Floridi, L. & Cowls, J. (2019). A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review 1(1).
Mentioned in
AI governanceAI governance, n.: The rules, institutions and habits that shape how AI is built and used — from laws and treaties to technical standards and a company's own review processes., AI safetyAI safety, n.: The effort to keep AI from doing serious harm, whether because it pursues aims its makers never intended, is turned to bad uses or simply malfunctions — a concern that grows as systems become more capable., AI for goodAI for good, n.: Using AI to tackle social and environmental problems, such as disease, poverty, disasters or climate change, rather than only to make a profit., ethics washingethics washing, n.: Using talk of ethics, such as principles, advisory boards or pledges, to look responsible and head off regulation, without actually changing what a company does., Joseph WeizenbaumJoseph Weizenbaum, prop. n.: A German-American computer scientist who built ELIZA, one of the first chatbots, and then became a leading critic of handing human judgment over to computers., machine ethicsmachine ethics, n.: The effort to build AI systems that can tell right from wrong and act ethically themselves, rather than only being used ethically by people., philosophy of artificial intelligencephilosophy of artificial intelligence, n.: The branch of philosophy that asks what AI is and could be: whether machines can think, understand or be conscious, what intelligence is, and what we owe to and should expect from intelligent machines., responsible AIresponsible AI, n.: An umbrella name for the practices organizations use to keep their AI systems fair, safe, private and accountable, from the first design meeting to everyday use., trolley problemtrolley problem, n.: A thought experiment asking whether you should divert a runaway tram so that it kills one person instead of five, often used to ask how self-driving cars should choose in an unavoidable crash., trustworthy AItrustworthy AI, n.: AI that deserves people's trust because it works reliably, obeys the law, treats people fairly and can be checked, rather than AI that people merely happen to trust., UNESCO Recommendation on the Ethics of AIUNESCO Recommendation on the Ethics of AI, prop. n.: A worldwide set of ethical principles for AI that all of UNESCO's member countries agreed in 2021, covering human rights, fairness, privacy, the environment, gender equality and education., woke AIwoke AI, n.: A politically charged label, used mainly by US conservatives, for AI systems seen as building progressive views into their answers. Since 2025 it also names a US rule on which AI the government may buy., AI Now InstituteAI Now Institute, prop. n.: A research and advocacy group that examines who is harmed by AI and argues that the deeper problem is the power of a few big technology companies, which it wants curbed by law., Asilomar AI PrinciplesAsilomar AI Principles, prop. n.: A list of 23 guidelines for safe and beneficial AI drawn up at a 2017 conference in California and signed by thousands of researchers and public figures., Distributed AI Research InstituteDistributed AI Research Institute, prop. n.: Timnit Gebru set up this institute in 2021, a year after her departure from Google, to research the damage AI can do, especially to marginalized communities, without relying on big-tech money., Ethics Guidelines for Trustworthy AIEthics Guidelines for Trustworthy AI, prop. n.: Guidelines written for the European Commission by an expert group in 2019, listing seven requirements AI should meet to be trustworthy; they paved the way for the EU AI Act., FAccTFAccT, prop. n.: A yearly academic conference on the fairness, accountability and transparency of computer systems, where much influential research on AI bias and ethics has been published., Kate CrawfordKate Crawford, prop. n.: An Australian scholar who studies the social, political and environmental costs of AI, co-founded the AI Now Institute, and wrote 'Atlas of AI'., Magnifica HumanitasMagnifica Humanitas, prop. n.: Pope Leo XIV's 2026 letter to the Catholic Church and the world about artificial intelligence, arguing that AI must serve human dignity and the common good., moral agencymoral agency, n.: The capacity to act for moral reasons and be held responsible for what one does. Researchers ask whether AI systems could ever be moral agents, or only tools whose makers and users bear responsibility., Partnership on AIPartnership on AI, prop. n.: A forum, set up by big technology companies in 2016 and later joined by universities, charities and news organizations, that drafts voluntary guidelines for building and using AI responsibly., value-sensitive designvalue-sensitive design, n.: A way of designing technology that deliberately identifies the human values at stake, such as privacy, fairness or autonomy, and builds them in from the start instead of patching problems later.