BaBE: Enhancing Fairness via Estimation of Latent Explaining Variables

TL;DR

BaBE is a pre-processing method that estimates latent explanatory variables using Bayesian inference and EM to improve fairness in decision-making, especially when direct observation of these variables is not possible.

Contribution

The paper introduces BaBE, a novel approach combining Bayesian inference and EM to estimate latent variables for fairness, addressing limitations of existing methods.

Findings

BaBE achieves better fairness than traditional methods.

The approach maintains high accuracy on synthetic and real datasets.

BaBE effectively estimates latent variables to improve fairness.

Abstract

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.