BaBE: Enhancing Fairness via Estimation of Latent Explaining Variables
Ruta Binkyte Daniele Gorla Catuscia Palamidessi
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.