responsible AI
In plain wordsAn 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.
An organizational approach that turns AI ethicsAI ethics, n.: The 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. principles into processes, roles and tools across the AI lifecycle: impact and risk assessments, fairness and robustness testing, documentation such as model cardsmodel card, n.: A short document published with an AI model explaining what it is for, how it was trained and tested, how well it works for different groups of people, and its known limits. and datasheetsdatasheet for datasets, n.: A standard document that travels with a dataset, describing why it was made, what is in it, how it was collected and labeled, and what it should and should not be used for., human oversighthuman oversight, n.: Keeping people able to watch, understand, question and, if necessary, stop or overrule an AI system, rather than letting it act unchecked., incident response, review boards and release sign-offs. Corporate frameworks differ in wording but return to the same few commitments, chiefly fairness, safety, privacy, transparency and accountability, and increasingly map them onto external requirements such as the NIST AI Risk Management FrameworkNIST AI Risk Management Framework, prop. n.: A free, voluntary guide from the US government's standards agency that helps organizations spot, measure and reduce the risks of the AI systems they build or use., the management-system standard ISO/IEC 42001ISO/IEC 42001, prop. n.: An international standard for how an organization should run its AI work responsibly — policies, risk checks, roles and records — which outside auditors can certify, much as ISO 9001 does for quality. and the EU AI ActEU AI Act, prop. n.: The European Union's law on artificial intelligence, the first broad AI law anywhere: it bans some uses, sets strict checks for risky ones and puts duties on the makers of the biggest AI models.. The term stresses the conduct and accountability of the developer or deployer, whereas 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. stresses properties of the system.
No model ships until the responsible AI team has signed off on the impact assessment.
Origin
Used sporadically before, the phrase took off in 2017–2019 as technology companies published AI principles (Microsoft and Google both in 2018) and created internal responsible-AI offices; Virginia Dignum's book Responsible Artificial Intelligence (2019) gave it an academic framing. By the 2020s it named a job family and a compliance function.
Usage note
Critics point out that responsible-AI teams have been cut when they conflicted with product goals (several large companies shrank or disbanded such teams in 2022–2023), and that self-governance without external enforcement risks 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
- AI ethicsAI ethics, n.: The 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.
- 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.
- 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.
- algorithmic impact assessmentalgorithmic impact assessment, n.: A structured review, done before and during the use of an automated system, of whom it could affect and how — such as unfair treatment or privacy harms — and what will be done about it.
- model cardmodel card, n.: A short document published with an AI model explaining what it is for, how it was trained and tested, how well it works for different groups of people, and its known limits.
- AI auditAI 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.
- human oversighthuman oversight, n.: Keeping people able to watch, understand, question and, if necessary, stop or overrule an AI system, rather than letting it act unchecked.
- 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.
Sources
- Dignum, V. (2019). Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way. Springer.
Mentioned in
AI ethicsAI ethics, n.: The 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., 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., algorithmic accountabilityalgorithmic accountability, n.: The idea that the people and organizations behind automated decision systems must be able to explain and justify those systems' effects, answer for the harm they cause, and fix it., 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., human-centered AIhuman-centered AI, n.: An approach to AI that puts people's needs, abilities and control at the center: systems designed to help humans do things better rather than to replace or overrule them., 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., Margaret MitchellMargaret Mitchell, prop. n.: An American computer scientist who proposed 'model cards' for documenting AI systems, co-led Google's Ethical AI team, and is chief ethics scientist at Hugging Face., 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.