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.

翻译：公平性在高风险机器学习应用（如医疗健康中的机器学习与面部识别）中日益关键。然而，我们发现现有逻辑空间约束方法存在缺陷。为此，我们提出Logits-MMD这一新型框架，通过应用最大均值差异对输出逻辑施加约束，实现公平性条件。进一步地，定量分析与实验结果表明，我们的框架具备更优特性，不仅超越先前方法，更在两个面部识别数据集与一个动物数据集上达到最优性能。最后，我们展示实验结果，论证所提出的去偏方法能有效实现公平性条件。