Abstract. 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 equality of opportunity and equalized odds metrics. A column generation framework is used to efficiently search over exponentially many possible rules, eliminating the need for heuristic rule mining.
Friday, April 8, 2022 – 11:00 to 12:00
ISyE Executive Board Room 228-Atlanta, GA
An integer programming approach for Fair and Interpretable Binary Classification