AI Leadership Assessment Governance Research Note

AI Leadership Assessment Governance Research Note

An AI leadership assessment needs one governance record across intended use, construct, reliability, validity, population, fairness, accessibility, privacy, security, explanation, appeal, human authority, model change, and monitoring.

NIST's AI Risk Management Framework treats valid and reliable, transparent, explainable, privacy-enhanced, and fair with harmful bias managed as connected trustworthiness properties. Its measurement guidance calls for testing before deployment, monitoring in operation, uncertainty, benchmarks, fairness documentation, and risk tracking.

A standardized prompt does not remove bias. Language can encode culture, disability, education, role, professional training, and opportunity. The output can also be used differently across reviewers.

Human oversight requires evidence, authority, time, training, accountability, and the ability to disagree. A human signature does not validate an unvalidated method.

A safe pilot runs in shadow mode and cannot cause hiring, promotion, funding, compensation, termination, or eligibility harm. It records disagreement, subgroup behavior, participant experience, uncertainty, later outcomes, and stopping criteria.

The assessed person needs notice, source visibility, correction, context, contest, and qualified review before consequential use.