Fair and Interpretable Decision Rules for Binary Classification

Oktay Gunluk
Cornell University

In this talk we consider the problem of building Boolean rule sets in disjunctive normal form (DNF), an interpretable model for binary classification, subject to fairness constraints. We formulate the problem as an integer program that maximizes classification accuracy with explicit constraints on group fairness. A column generation framework, with a novel formulation, is used to efficiently search over exponentially many possible rules, eliminating the need for heuristic rule mining. Compared to other interpretable machine learning algorithms, our method produces interpretable classifiers that have superior performance with regards to fairness/accuracy tradeoff.

This is joint work with Connor Lawless.

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