FairSAM: Fair Classification on Corrupted Data Through Sharpness-Aware Minimization

TMLR Paper7559 Authors

Abstract: Image classification models trained on clean data often suffer from significant performance degradation when exposed to corrupted testing or deployment data, such as images with impulse noise, Gaussian noise, or environmental noise. This degradation not only impacts overall performance but also disproportionately affects various demographic subgroups, raising critical algorithmic bias concerns. Although robust learning algorithms such as Sharpness-Aware Minimization improve overall model robustness and generalization, they do not address biased performance degradation across demographic subgroups. Existing fairness-aware machine learning methods aim to reduce performance disparities but struggle to maintain robust and equitable accuracy across demographic subgroups when faced with data corruption. This reveals an inherent tension between robustness and fairness when dealing with corrupted data. To address these challenges, we introduce a newly-designed metric to assess performance degradation across subgroups under data corruption. We propose FairSAM, a framework that integrates Fairness-oriented strategies into SAM to deliver equalized performance across demographic groups under corrupted conditions. Our experiments on multiple real-world datasets and various predictive tasks show that FairSAM reconciles robustness and fairness. The framework yields a structured solution for fair and robust image classification in the presence of data corruption.

Submission Type: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Qi_CHEN6

Submission Number: 7559