Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns
This work addresses fairness in algorithmic decision-making for applications using naive Bayes classifiers, particularly when sensitive attributes may not be observed at test time, representing an incremental advance in fairness methods.
The paper tackled fairness in naive Bayes classifiers by introducing discrimination patterns, which occur when classifications differ based on the observation of sensitive attributes, and proposed an algorithm to discover and eliminate these patterns to learn fair models. Empirical results on three real-world datasets showed that removing exponentially many discrimination patterns required adding only a small fraction as constraints.
As machine learning is increasingly used to make real-world decisions, recent research efforts aim to define and ensure fairness in algorithmic decision making. Existing methods often assume a fixed set of observable features to define individuals, but lack a discussion of certain features not being observed at test time. In this paper, we study fairness of naive Bayes classifiers, which allow partial observations. In particular, we introduce the notion of a discrimination pattern, which refers to an individual receiving different classifications depending on whether some sensitive attributes were observed. Then a model is considered fair if it has no such pattern. We propose an algorithm to discover and mine for discrimination patterns in a naive Bayes classifier, and show how to learn maximum likelihood parameters subject to these fairness constraints. Our approach iteratively discovers and eliminates discrimination patterns until a fair model is learned. An empirical evaluation on three real-world datasets demonstrates that we can remove exponentially many discrimination patterns by only adding a small fraction of them as constraints.