Beyond Predictive Fairness: Quantifying Attribution Consistency Across Demographic Groups in Diabetic Retinopathy Screening

2026-08-19

AI Summary

研究通过引入基于Jensen-Shannon散度的解释一致性分数(ECS)来量化糖尿病视网膜病变筛查中不同人群归因图的一致性，以补充预测公平性的评估。

Abstract

Fairness in medical imaging is commonly evaluated through subgroup performance metrics, yet it remains unclear whether models rely on consistent visual evidence across demographic groups. This work introduces the Explanation Consistency Score (ECS), a fairness-aware metric based on Jensen-Shannon divergence that quantifies the similarity of attribution maps across subgroups. Using diabetic retinopathy screening as a case study, ECS is evaluated globally and within disease severity. Experiments reveal that while predictive performance differs across ethnic groups, explanation consistency remains relatively high and shows no significant association with performance disparities. These findings suggest that predictive fairness and explanation consistency capture complementary dimensions of model behavior, motivating fairness evaluations that extend beyond predictive performance.

Problem

• Fairness
• Medical Imaging
• Attribution Consistency
• Demographic Groups
• Diabetic Retinopathy Screening

Innovation

• Explanation Consistency Score (ECS)
• Jensen-Shannon divergence
• attribution maps
• diabetic retinopathy screening
• fairness-aware metric