Test-fairness deep learning with influence score

Performance disparities in AI systems can manifest across sensitive groups or across data sources, especially when training data are collected from specific populations. In this work, we propose a feature-selection-based method that improves test-fairness while preserving prediction performance. Built on deep learning models, the proposed approach adopts the influence score (I-score), a statistical measure that captures interaction effects among multiple features. We identify features strongly associated with dataset membership by training an auxiliary model to predict dataset origin and applying I-score-based subset selection; these dataset-associated features are then excluded (masked) from the original prediction model for follow-up inference. We conduct experiments on two skin lesion datasets, ISIC 2019 and ASAN, collected from different populations. The empirical results show that the resulting fair I-score model can maintain high classification performance for skin lesion prediction while reducing cross-dataset subgroup performance disparity under our test-fairness evaluation setting.