A Distributionally Robust Optimisation Approach to Fair Credit Scoring
Pablo Casas Christophe Mues Huan Yu
Credit scoring has been catalogued by the European Commission and the
Executive Office of the US President as a high-risk classification task, a key
concern being the potential harms of making loan approval decisions based on
models that would be biased against certain groups. To address this concern,
recent credit scoring research has considered a range of fairness-enhancing
techniques put forward by the machine learning community to reduce bias and
unfair treatment in classification systems. While the definition of fairness or
the approach they follow to impose it may vary, most of these techniques,
however, disregard the robustness of the results. This can create situations
where unfair treatment is effectively corrected in the training set, but when
producing out-of-sample classifications, unfair treatment is incurred again.
Instead, in this paper, we will investigate how to apply Distributionally
Robust Optimisation (DRO) methods to credit scoring, thereby empirically
evaluating how they perform in terms of fairness, ability to classify
correctly, and the robustness of the solution against changes in the marginal
proportions. In so doing, we find DRO methods to provide a substantial
improvement in terms of fairness, with almost no loss in performance. These
results thus indicate that DRO can improve fairness in credit scoring, provided
that further advances are made in efficiently implementing these systems. In
addition, our analysis suggests that many of the commonly used fairness metrics
are unsuitable for a credit scoring setting, as they depend on the choice of
classification threshold.