Toward Fairness via Maximum Mean Discrepancy Regularization on Logits Space
<|reference_start|>Toward Fairness via Maximum Mean Discrepancy Regularization on Logits Space: 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.<|reference_end|>
@article{chung2024toward,
    title={Toward Fairness via Maximum Mean Discrepancy Regularization on Logits
  Space},
    author={Hao-Wei Chung, Ching-Hao Chiu, Yu-Jen Chen, Yiyu Shi, Tsung-Yi Ho},
    journal={arXiv preprint arXiv:2402.13061},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13061},
    primaryClass={cs.CV}
}