论文标题
可实现和最佳性：均衡的赔率公平性重新审视
Attainability and Optimality: The Equalized Odds Fairness Revisited
论文作者
论文摘要
机器学习算法的公平性一直引起人们的兴趣。为了抑制或消除预测中的歧视，已经提出了各种概念以及施加公平性的方法。考虑到公平的概念，即使使用无限的数据，也可以始终达到一个基本问题。但是，这个问题尚未得到很好的解决。在本文中，着眼于公平的均等几率概念，我们考虑了该标准的可实现性，此外，如果可以实现，则在各种设置下的预测绩效的最佳性。特别是，对于通过输入特征的确定性函数执行的预测，我们给出了均衡赔率可以保持真实的条件；如果随机预测是可以接受的，我们表明在温和的假设下，可以始终得出公平的预测因子。对于分类，我们进一步证明，与通过后处理实施公平性相比，人们总是可以从训练过程中利用所有可用功能并获得更好的预测性能，同时保持公平。此外，尽管随机预测可以与理论保证达到均等的赔率，但我们还讨论了其限制和潜在的负面社会影响。
Fairness of machine learning algorithms has been of increasing interest. In order to suppress or eliminate discrimination in prediction, various notions as well as approaches have been proposed to impose fairness. Given a notion of fairness, an essential problem is then whether or not it can always be attained, even if with an unlimited amount of data. This issue is, however, not well addressed yet. In this paper, focusing on the Equalized Odds notion of fairness, we consider the attainability of this criterion and, furthermore, if it is attainable, the optimality of the prediction performance under various settings. In particular, for prediction performed by a deterministic function of input features, we give conditions under which Equalized Odds can hold true; if the stochastic prediction is acceptable, we show that under mild assumptions, fair predictors can always be derived. For classification, we further prove that compared to enforcing fairness by post-processing, one can always benefit from exploiting all available features during training and get potentially better prediction performance while remaining fair. Moreover, while stochastic prediction can attain Equalized Odds with theoretical guarantees, we also discuss its limitation and potential negative social impacts.
