  In recent years, machine learning has begun automating decision making in
fields as varied as college admissions, credit lending, and criminal
sentencing. The socially sensitive nature of some of these applications
together with increasing regulatory constraints has necessitated the need for
algorithms that are both fair and interpretable. In this paper 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 two different measures of classification
parity: equality of opportunity and equalized odds. Column generation
framework, with a novel formulation, is used to efficiently search over
exponentially many possible rules. When combined with faster heuristics, our
method can deal with large data-sets. Compared to other fair and interpretable
classifiers, our method is able to find rule sets that meet stricter notions of
fairness with a modest trade-off in accuracy.
