Three naive Bayes approaches for discrimination-free classification

TLDR

Discrimination restrictions arise when training data is biased, as laws prohibit decisions partly based on protected attributes, and naive machine learning can incur fines. The study investigates how to modify the naive Bayes classifier to enforce independence from a sensitive attribute. The authors propose three methods—adjusting the positive decision probability, training separate balanced models per sensitive value, and adding a latent unbiased label optimized via EM—and evaluate them on synthetic and real data.

Abstract

In this paper, we investigate how to modify the naive Bayes classifier in order to perform classification that is restricted to be independent with respect to a given sensitive attribute. Such independency restrictions occur naturally when the decision process leading to the labels in the data-set was biased; e.g., due to gender or racial discrimination. This setting is motivated by many cases in which there exist laws that disallow a decision that is partly based on discrimination. Naive application of machine learning techniques would result in huge fines for companies. We present three approaches for making the naive Bayes classifier discrimination-free: (i) modifying the probability of the decision being positive, (ii) training one model for every sensitive attribute value and balancing them, and (iii) adding a latent variable to the Bayesian model that represents the unbiased label and optimizing the model parameters for likelihood using expectation maximization. We present experiments for the three approaches on both artificial and real-life data.