Why Aggregate Accuracy Fails in High-Stakes AI
Why strong overall accuracy can conceal subgroup failures and hidden deployment risks.
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Research notes on operational AI assurance and deployment governance.
Technical perspectives exploring operational assurance, subgroup reliability, fairness disagreement, threshold instability, deployment risk, and governance considerations in high-stakes AI systems.
When Fairness Metrics Disagree
Why different fairness metrics can produce conflicting conclusions about the same model.
Read InsightFalse Positives vs False Negatives
Why deployment context changes which AI system errors matter most.
Read InsightThe Hidden Risk of Subgroup Failure
Why aggregate metrics can hide reliability disparities affecting specific populations.
Read InsightWhy Responsible AI Needs Operational Assurance
Why responsible AI requires structured operational evaluation beyond high-level principles.
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