Quantitative measures (e.g., accuracy drift, fairness scores, incident response time) used to monitor AI system health, risk, and compliance objectives.
A dashboard-driven set of indicators - model-specific (accuracy, latency), governance-specific (percentage of models with current impact assessments), and organizational (mean time to bias remediation). Metrics are reviewed at defined cadences by governance bodies and linked to strategic goals, enabling data-driven decisions on resource allocation, process improvements, and risk prioritization.
A tech company’s AI Governance Scorecard includes KPIs: 100% of production models have up-to-date validation, average incident response time under 24 hours, and monthly fairness-metric trends. The Data Ethics Council reviews these metrics quarterly to guide policy updates.
