  We study the problem of selecting the top-k candidates from a pool of
applicants, where each candidate is associated with a score indicating his/her
aptitude. Depending on the specific scenario, such as job search or college
admissions, these scores may be the results of standardized tests or other
predictors of future performance and utility. We consider a situation in which
some groups of candidates experience historical and present disadvantage that
makes their chances of being accepted much lower than other groups. In these
circumstances, we wish to apply an affirmative action policy to reduce
acceptance rate disparities, while avoiding any large decrease in the aptitude
of the candidates that are eventually selected. Our algorithmic design is
motivated by the frequently observed phenomenon that discrimination
disproportionately affects individuals who simultaneously belong to multiple
disadvantaged groups, defined along intersecting dimensions such as gender,
race, sexual orientation, socio-economic status, and disability. In short, our
algorithm's objective is to simultaneously: select candidates with high
utility, and level up the representation of disadvantaged intersectional
classes. This naturally involves trade-offs and is computationally challenging
due to the the combinatorial explosion of potential subgroups as more
attributes are considered. We propose two algorithms to solve this problem,
analyze them, and evaluate them experimentally using a dataset of university
application scores and admissions to bachelor degrees in an OECD country. Our
conclusion is that it is possible to significantly reduce disparities in
admission rates affecting intersectional classes with a small loss in terms of
selected candidate aptitude. To the best of our knowledge, we are the first to
study fairness constraints with regards to intersectional classes in the
context of top-k selection.
