Learning Optimal Fair Classification Trees

The increasing use of machine learning in high-stakes domains -- where people's livelihoods are impacted -- creates an urgent need for interpretable and fair algorithms. In these settings it is also critical for such algorithms to be accurate. With these needs in mind, we propose a mixed integer optimization (MIO) framework for learning optimal classification trees of fixed depth that can be conveniently augmented with arbitrary domain specific fairness constraints. We benchmark our method against the state-of-the-art approach for building fair trees on popular datasets; given a fixed discrimination threshold, our approach improves out-of-sample (OOS) accuracy by 2.3 percentage points on average and obtains a higher OOS accuracy on 88.9% of the experiments. We also incorporate various algorithmic fairness notions into our method, showcasing its versatile modeling power that allows decision makers to fine-tune the trade-off between accuracy and fairness.