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以实现公平。统计平权等纠正性方法通常导致较差准确性，且在敏感属性S与应决定决策的合法属性E（解释变量）存在关联时无法真正实现公平。为解决这些弊端，学界提出了其他公平性概念，尤其是条件统计平权和机会均等。然而，E在数据中往往无法直接观测，即其属于潜变量。我们可能观察到某些代表E的变量Z，但问题在于Z可能受S影响而存在偏差。针对该问题，我们提出BaBE（贝叶斯偏差消除）方法——一种融合贝叶斯推断与期望最大化（EM）算法的方法，用以估计每组在给定Z条件下E的最可能取值。决策可直接基于估计的E进行。通过在合成数据集和真实数据集上的实验证明，本方法能实现良好的公平性水平与高准确性。