Explainable Disparity Compensation for Efficient Fair Ranking

TL;DR

This paper introduces explainable, data-driven methods for fair ranking that use bonus points to mitigate disparities, offering transparency and efficiency validated on real-world datasets.

Contribution

It proposes novel, explainable bonus point mechanisms for fair ranking, with efficient algorithms and validation on real-world data, improving transparency over prior opaque methods.

Findings

Effective disparity reduction demonstrated on school admissions data

Comparable or improved fairness metrics relative to existing algorithms

Enhanced transparency through explainable bonus point system

Abstract

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.