Gradient-Based Reconciliation of Fairness and Performance in Healthcare AI

Abstract

The enrichment of healthcare data coupled with advancements in computational capabilities has driven the adoption of artificial intelligence in healthcare. However, these approaches, when implemented without fairness considerations, may exacerbate existing disparities, leading to inequitable resource allocation and diagnostic inaccuracies across different demographic groups. This study introduces a novel approach via gradient projection to multi-attribute fairness optimization in healthcare AI, optimizing fairness across multiple demographic attributes and predictive performance concurrently. The approach aligns conflicting optimization objectives by projecting each gradient onto the normal plane of the other. Our method also enhances interpretability by elucidating the adjustments made during optimization, providing insights into trade-offs between fairness and accuracy.

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