FAIR DECISION MAKING VIA AUTOMATED REPAIR OF DECISION TREES

Abstract

Data-driven decision-making allows more resource allocation tasks to be done by programs. Real-life training datasets may capture human biases, and the learned models can be unfair. One could either train a new, fair model from scratch or repair an existing unfair model. The former is liable for unbounded semantic difference, hence is unsuitable for social or legislative decisions. Meanwhile, the scalability of state-of-the-art model repair techniques is unsatisfactory.

We aim to automatically repair unfair decision models by converting any decision tree or random forest into a fair one with respect to a specific dataset and sensitive attributes. We built the FairRepair tool, inspired by automated program repair techniques for traditional programs. It uses a MaxSMT solver to decide which paths in the decision tree could be flipped or refined, with both fairness and semantic difference as hard constraints. Our approach is sound and complete.