Achieving Fairness Without Harm via Selective Demographic Experts
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909894286770176 |
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| author | Tan, Xuwei Wang, Yuanlong Pham, Thai-Hoang Zhang, Ping Zhang, Xueru |
| author_facet | Tan, Xuwei Wang, Yuanlong Pham, Thai-Hoang Zhang, Ping Zhang, Xueru |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_06293 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Achieving Fairness Without Harm via Selective Demographic Experts Tan, Xuwei Wang, Yuanlong Pham, Thai-Hoang Zhang, Ping Zhang, Xueru Machine Learning 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. |
| title | Achieving Fairness Without Harm via Selective Demographic Experts |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.06293 |