Explainable Disparity Compensation for Efficient Fair Ranking
Abraham Gale Amélie Marian
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.