Test of Time Award for CYS paper

Toon Calders and Sicco Verwer receive the Test of Time Award for Three naive Bayes approaches for discrimination-free classification.

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.

Praise from the Awards Chairs: A highly-influential, visionary paper on discrimination-free or fair classification, ahead of the current trend on fairness, accountability and transparency in AI that is transforming the algorithmic decision-making process in numerous areas, including law, education, banking, health, and more.