In this work, we de ne 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 de nition extends group fairness using the standard notion of protected groups and is based on ensuring that the proportion of protected candidates in every pre x 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 quali ed than every candidate not included; and (ii) for every pair of candidates in the top-k, the more quali ed candidate should be ranked above. An e cient 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 rst algorithm grounded in statistical tests that can mitigate biases in the representation of an under-represented group along a ranked list.
Fairness in top-k over a single binary type attribute (such as gender, ethnic majority/minority, or disability status) is studied in Zehlike et al. REF , where the goal is to ensure that the proportion of members of a protected group in every prefix of the ranking remains statistically above a given minimum.
FA*IR: A Fair Top-k Ranking Algorithm
CIKM '17
Computer Science