Three ethical artificial intelligence metrics managers can track

Transcript

Here is a quick manager guide to ethical artificial intelligence metrics. For fairness, track false positive and false negative rates by demographic group, then compare the worst and best groups with a gap or ratio. Add bias test coverage across the full lifecycle, including tests that change protected attributes. Tip: review fairness together with accuracy, because one number alone does not tell the whole story. For transparency, measure documentation completeness, traceability, explainability coverage, freshness, and whether users can find clear system descriptions, limits, contact details, and redress paths. Tip: keep records current after each model, data, or policy change. For safety, watch incident rate and severity, adversarial testing coverage, production monitoring coverage, time to detect and repair, and whether corrective actions restore safe function. Tip: set thresholds and limits for each measure in your specific use case, document what you measure and why, and review the scorecard in human and organizational context, not as isolated numbers.