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arXiv · 2402.13061

Toward Fairness via Maximum Mean Discrepancy Regularization on Logits Space

Hao-Wei Chung·Ching-Hao Chiu·Yu-Jen Chen·Yiyu Shi·Tsung-Yi Ho
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Abstract

Fairness has become increasingly pivotal in machine learning for high-risk applications such as machine learning in healthcare and facial recognition. However, we see the deficiency in the previous logits space constraint methods. Therefore, we propose a novel framework, Logits-MMD, that achieves the fairness condition by imposing constraints on output logits with Maximum Mean Discrepancy. Moreover, quantitative analysis and experimental results show that our framework has a better property that outperforms previous methods and achieves state-of-the-art on two facial recognition datasets and one animal dataset. Finally, we show experimental results and demonstrate that our debias approach achieves the fairness condition effectively.

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Hao-Wei Chung, Ching-Hao Chiu, Yu-Jen Chen, Yiyu Shi, Tsung-Yi Ho. 2024-02-20. Toward Fairness via Maximum Mean Discrepancy Regularization on Logits Space. https://arxiv.org/abs/2402.13061

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