Responsible AI — AI Glossary

Reference details

Related terms

• AI Governance
• AI Alignment
• EU AI Act

Example: A principle, made testable

“Be fair” cannot be checked. “Approval rate for equivalent applications must not differ by more than 2 percentage points across protected groups, measured monthly on held-out cases, with results retained for audit” can be. Responsible AI becomes real at the point a principle acquires a metric, a threshold, an owner and a review cadence.

Commonly confused with

Responsible AI is not the same as AI governance, though the terms are used interchangeably. Responsible AI names the principles — fairness, accountability, transparency. Governance is the machinery that enforces them: policies, roles, approvals, audit trails. Principles without machinery are a statement of intent.

When to use it

Reach for it when:

• The system makes or shapes decisions about people
• You will need to show a regulator or customer how a decision was reached
• Failures would fall unevenly across groups rather than randomly

Reach for something else when:

• As a published commitment with nothing measuring it — that invites the scrutiny without the defence
• As a substitute for a specific legal obligation; the EU AI Act asks for particular artefacts

Referenced by

This term is used by the following ModelRefs references:

• become ai architect

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