Achieving Fairness Without Harm via Selective Demographic Experts

• URL: http://arxiv.org/abs/2511.06293v1
• Date: Sun, 09 Nov 2025 09:11:02 GMT
• Title: Achieving Fairness Without Harm via Selective Demographic Experts
• Authors: Xuwei Tan, Yuanlong Wang, Thai-Hoang Pham, Ping Zhang, Xueru Zhang,
• Abstract summary: bias mitigation techniques often impose a trade-off between fairness and accuracy.<n>In high-stakes domains like clinical diagnosis, such trade-offs are ethically and practically unacceptable.<n>We propose a fairness-without-harm approach by learning distinct representations for different demographic groups.
• Score: 16.212815178841087
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As machine learning systems become increasingly integrated into human-centered domains such as healthcare, ensuring fairness while maintaining high predictive performance is critical. Existing bias mitigation techniques often impose a trade-off between fairness and accuracy, inadvertently degrading performance for certain demographic groups. In high-stakes domains like clinical diagnosis, such trade-offs are ethically and practically unacceptable. In this study, we propose a fairness-without-harm approach by learning distinct representations for different demographic groups and selectively applying demographic experts consisting of group-specific representations and personalized classifiers through a no-harm constrained selection. We evaluate our approach on three real-world medical datasets -- covering eye disease, skin cancer, and X-ray diagnosis -- as well as two face datasets. Extensive empirical results demonstrate the effectiveness of our approach in achieving fairness without harm.

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