Glossary
Responsible AI
A practice-oriented label for designing, deploying and overseeing AI systems in ways intended to reduce harm and align with stated values, rights and laws.
Last reviewed 2026-08-22
In plain language
“Responsible AI” is widely used by companies, governments and researchers. It typically bundles risk management, documentation, human oversight, fairness testing and incident response. The term is not standardized in international law. Different actors define responsibility through their own policies, which may or may not map onto human-rights due diligence or statutory duties. In practice, an organization's responsible-AI program is usually an internal operationalization of an external framework: a company might structure its policy around the four functions of the NIST AI Risk Management Framework (Govern, Map, Measure, Manage), or a public agency might publish a responsible-AI office charter that assigns named owners to each risk category. The label describes a practice, not a credential—it should not be confused with AI assurance, which is the independent or internal evidence that a specific responsible-AI claim actually holds, or with AI governance, the broader institutional map of who has authority to set and enforce these practices in the first place. It is also distinct from trustworthy AI, a term used mainly in European Commission documents for a related but separately defined set of requirements. Because “responsible AI” carries no fixed legal content, the substantive governance question is always which concrete controls, named owners and remedies a program actually includes, not whether the label is used.
Why it matters for AI governance
The phrase can describe genuine operational controls or function as branding. Governance work should ask which concrete processes, metrics and remedies sit behind the label.