Explainable Disparity Compensation for Efficient Fair Ranking
This addresses fairness in decision-making systems like admissions and criminal justice, but it is incremental as it builds on prior compensatory methods by focusing on explainability.
The paper tackles the problem of bias in ranking functions by proposing explainable compensatory measures that assign bonus points to underrepresented groups to reduce disparity, and validates the approach on school admissions and recidivism datasets with comparisons to existing fair ranking algorithms.
Ranking functions that are used in decision systems often produce disparate results for different populations because of bias in the underlying data. Addressing, and compensating for, these disparate outcomes is a critical problem for fair decision-making. Recent compensatory measures have mostly focused on opaque transformations of the ranking functions to satisfy fairness guarantees or on the use of quotas or set-asides to guarantee a minimum number of positive outcomes to members of underrepresented groups. In this paper we propose easily explainable data-driven compensatory measures for ranking functions. Our measures rely on the generation of bonus points given to members of underrepresented groups to address disparity in the ranking function. The bonus points can be set in advance, and can be combined, allowing for considering the intersections of representations and giving better transparency to stakeholders. We propose efficient sampling-based algorithms to calculate the number of bonus points to minimize disparity. We validate our algorithms using real-world school admissions and recidivism datasets, and compare our results with that of existing fair ranking algorithms.