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We consider the problem of unfair discrimination between two groups andpropose a pre-processing method to achieve fairness. Corrective methods likestatistical parity usually lead to bad accuracy and do not really achievefairness in situations where there is a correlation between the sensitiveattribute S and the legitimate attribute E (explanatory variable) that shoulddetermine the decision. To overcome these drawbacks, other notions of fairnesshave been proposed, in particular, conditional statistical parity and equalopportunity. However, E is often not directly observable in the data, i.e., itis a latent variable. We may observe some other variable Z representing E, butthe 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), anapproach based on a combination of Bayes inference and theExpectation-Maximization method, to estimate the most likely value of E for agiven Z for each group. The decision can then be based directly on theestimated E. We show, by experiments on synthetic and real data sets, that ourapproach provides a good level of fairness as well as high accuracy.

BaBE: Enhancing Fairness via Estimation of Latent Explaining Variables