# FA*IR: A Fair Top-k Ranking Algorithm

## TL;DR

This paper introduces a novel algorithm for fair top-k ranking that balances utility with group fairness, ensuring protected groups are proportionally represented while maintaining high-quality rankings.

## Contribution

It presents the first statistically grounded algorithm for mitigating biases in ranked lists, balancing fairness and utility in top-k selection.

## Key findings

- The algorithm effectively maintains group fairness in rankings.
- Experimental results show minimal utility distortion.
- The approach outperforms existing methods in fairness metrics.

## Abstract

In this work, we define and solve the Fair Top-k Ranking problem, in which we want to determine a subset of k candidates from a large pool of n >> k candidates, maximizing utility (i.e., select the "best" candidates) subject to group fairness criteria. Our ranked group fairness definition extends group fairness using the standard notion of protected groups and is based on ensuring that the proportion of protected candidates in every prefix of the top-k ranking remains statistically above or indistinguishable from a given minimum. Utility is operationalized in two ways: (i) every candidate included in the top-$k$ should be more qualified than every candidate not included; and (ii) for every pair of candidates in the top-k, the more qualified candidate should be ranked above. An efficient algorithm is presented for producing the Fair Top-k Ranking, and tested experimentally on existing datasets as well as new datasets released with this paper, showing that our approach yields small distortions with respect to rankings that maximize utility without considering fairness criteria. To the best of our knowledge, this is the first algorithm grounded in statistical tests that can mitigate biases in the representation of an under-represented group along a ranked list.