论文标题
公平ML的新颖正规化方法
A Novel Regularization Approach to Fair ML
论文作者
论文摘要
为公平的ML问题引入了许多方法，其中大多数是复杂的，并且其中许多非常针对基础ML Moethodology。在这里，我们介绍了一种简单，易于解释的新方法，并可能适用于许多标准ML算法。显式降级功能（EDF）降低了敏感变量代理之间每个功能的影响，从而使对每个此类功能应用了不同量的降级。用户指定脱水超参数，以达到实用性/公平性权衡频谱中的给定点。我们还引入了一个新的简单标准，用于评估任何公平的ML方法提供的保护程度。
A number of methods have been introduced for the fair ML issue, most of them complex and many of them very specific to the underlying ML moethodology. Here we introduce a new approach that is simple, easily explained, and potentially applicable to a number of standard ML algorithms. Explicitly Deweighted Features (EDF) reduces the impact of each feature among the proxies of sensitive variables, allowing a different amount of deweighting applied to each such feature. The user specifies the deweighting hyperparameters, to achieve a given point in the Utility/Fairness tradeoff spectrum. We also introduce a new, simple criterion for evaluating the degree of protection afforded by any fair ML method.
