On the Within-Group Fairness of Screening Classifiers
Jul 24, 2023
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Nastaran Okati
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Stratis Tsirtsis
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Manuel Gomez-Rodriguez
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About
Screening classifiers are increasingly used to identify qualified candidates in a variety of selection processes.In this context, it has been recently shown that, if a classifier is calibrated, there exists a screening policy taking shortlisting decisions by thresholding the classifier output that is guaranteed to identify the smallest set of candidates which contains, in expectation, a desired number of qualified candidates.This lends support to focusing on calibration as the only requirement for screening classifiers.In this paper, we argue that screening policies using calibrated classifiers may suffer from an understudied type of within-group discrimination—they may discriminate against qualified members within demographic groups of interest.Further, we argue that this type of discrimination can be avoided if classifiers satisfy within-group monotonicity, a natural monotonicity property within each of the groups.Then, we introduce an efficient post-processing algorithm based on dynamic programming to minimally modify a given calibrated classifier so that its probability estimates satisfy within-group monotonicity. We validate our algorithm using US Census survey data and show that within-group monotonicity can be often achieved at a small cost in terms of prediction granularity and shortlist size.Screening classifiers are increasingly used to identify qualified candidates in a variety of selection processes.In this context, it has been recently shown that, if a classifier is calibrated, there exists a screening policy taking shortlisting decisions by thresholding the classifier output that is guaranteed to identify the smallest set of candidates which contains, in expectation, a desired number of qualified candidates.This lends support to focusing on calibration as the only requirement f…
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