Responsible artificial intelligence applies technical, organizational and ethical controls across an AI system's lifecycle. It considers safety, fairness, privacy, transparency, reliability, human oversight and the real effects of a system on users and communities.
Responsibility cannot be delegated to a model. Teams must define the intended scope, test how unfamiliar users understand outputs, preserve safeguards and correct distortions before scaling. The appropriate controls depend on the system's impact and must remain visible in ordinary product decisions.
Acronyms and aliases
responsible AI variantresponsible artificial intelligence practices variant
Specialised terms
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Frequently asked questions
What are the main parts of responsible AI?
Common parts include safety, fairness, privacy, transparency, security, reliability, human oversight and accountable governance. Their priority depends on the use and affected people.
Who is responsible for an AI system?
The people and organizations that design, deploy and operate it retain responsibility. A model cannot accept legal, ethical or product accountability for its own output.
