Quality-of-Service Harms

Mitigation strategy

1. Implement Post-Processing Bias Mitigation: Apply post-deployment methods, such as threshold adjustment or reject option classification, to the algorithmic system's outputs to directly minimize disparities in performance metrics, like Equal Opportunity Difference, across the affected social categories of difference. 2. Establish Continuous Performance Monitoring and Auditing: Mandate robust governance structures to continuously audit and monitor system performance in the live environment for evidence of disparate impact, specifically focusing on fine-grained and intersectional subgroup analyses to preemptively detect unequal outcomes. 3. Prioritize Remedial Data Rebalancing and Retraining: Based on identified performance disparities, conduct targeted data rebalancing, such as protected-category oversampling, and employ bias-aware retraining strategies to improve model generalization and predictive accuracy for the disproportionately affected populations.