We consider the problem of unfair discrimination between two groups and propose a pre-processing method to achieve fairness. Corrective methods like statistical parity usually lead to bad accuracy and do not really achieve fairness in situations where there is a correlation between the sensitive attribute S and the legitimate attribute E (explanatory variable) that should determine the decision. To overcome these drawbacks, other notions of fairness have been proposed, in particular, conditional statistical parity and equal opportunity. However, E is often not directly observable in the data, i.e., it is a latent variable. We may observe some other variable Z representing E, but the problem is that Z may also be affected by S, hence Z itself can be biased. To deal with this problem, we propose BaBE (Bayesian Bias Elimination), an approach based on a combination of Bayes inference and the Expectation-Maximization method, to estimate the most likely value of E for a given Z for each group. The decision can then be based directly on the estimated E. We show, by experiments on synthetic and real data sets, that our approach provides a good level of fairness as well as high accuracy.

翻译：我们考虑两组间不公平歧视问题，并提出一种预处理方法实现公平性。当敏感属性S与应决定决策的合法属性E（解释变量）存在相关性时，统计均等性等矫正方法通常会导致较低准确率，且无法真正实现公平。为克服此缺陷，学界提出了其他公平性概念，特别是条件统计均等性与机会均等。然而，E通常在数据中无法直接观测，属于潜在变量。我们可能观测到表征E的其他变量Z，但问题在于Z也可能受S影响因而存在偏差。为处理此问题，我们提出BaBE（贝叶斯偏差消除）方法，基于贝叶斯推断与期望最大化方法的结合，为各群体估计给定Z下最可能的E值。由此可直接基于估计值E进行决策。通过合成数据集与真实数据集的实验表明，我们的方法在保持较高准确率的同时实现了良好的公平性水平。