Title: FA*IR: A Fair Top-k Ranking Algorithm,
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.