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The subjective review of AI system behaviors, decisions, and documentation by experts to identify ethical, legal, or reputational concerns not captured quantitatively.
Complements quantitative metrics with expert judgment - ethics panels, legal reviews, user-experience studies - to uncover issues like cultural insensitivity, deceptive UX patterns, or legal ambiguities. Governance embeds qualitative assessments at key milestones (design, pilot, post-release), documents findings in review reports, and tracks remediation actions to address narrative or contextual risks beyond what metrics capture.
Real world example:
During design of a facial-recognition tool, a panel of legal and civil-rights experts conducts a qualitative assessment - identifying potential misuse for unauthorized surveillance. Based on their feedback, the vendor adds explicit user-consent flows and strict audit-logging, rather than relying solely on accuracy and bias metrics.
