{"authors":[{"id":null,"fullName":"Okati, N.","name":"N.","surname":"Okati","rank":1,"pid":null},{"id":null,"fullName":"Tsirtsis, S.","name":"S.","surname":"Tsirtsis","rank":2,"pid":null},{"id":null,"fullName":"Gomez Rodriguez, M. ; https://orcid.org/0000-0003-3930-1161","name":"M. Https Orcid Org -. -. -.","surname":"Gomez Rodriguez","rank":3,"pid":null}],"openAccessColor":null,"publiclyFunded":false,"eoscIfGuidelines":null,"type":"publication","language":{"code":"und","label":"Undetermined"},"countries":null,"subjects":[{"subject":{"scheme":"keyword","value":"FOS: Computer and information sciences"},"provenance":null},{"subject":{"scheme":"keyword","value":"Computer Science - Machine Learning"},"provenance":null},{"subject":{"scheme":"keyword","value":"Computer Science - Computers and Society"},"provenance":null},{"subject":{"scheme":"keyword","value":"Statistics - Machine Learning"},"provenance":null},{"subject":{"scheme":"keyword","value":"Computer Science - Data Structures and Algorithms"},"provenance":null},{"subject":{"scheme":"keyword","value":"Computers and Society (cs.CY)"},"provenance":null},{"subject":{"scheme":"FOS","value":"0202 electrical engineering, electronic engineering, information engineering"},"provenance":null},{"subject":{"scheme":"keyword","value":"Data Structures and Algorithms (cs.DS)"},"provenance":null},{"subject":{"scheme":"keyword","value":"Machine Learning (stat.ML)"},"provenance":null},{"subject":{"scheme":"FOS","value":"02 engineering and technology"},"provenance":null},{"subject":{"scheme":"keyword","value":"Machine Learning (cs.LG)"},"provenance":null}],"mainTitle":"On the Within-Group Fairness of Screening Classifiers","subTitle":null,"descriptions":["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, one can identify the smallest set of candidates which contains, in expectation, a desired number of qualified candidates using a threshold decision rule. This lends support to focusing on calibration as the only requirement for screening classifiers. In this paper, we argue that screening policies that use calibrated classifiers may suffer from an understudied type of within-group unfairness -- they may unfairly treat qualified members within demographic groups of interest. Further, we argue that this type of unfairness 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."],"publicationDate":"2023-01-01","publisher":null,"embargoEndDate":"2023-02-01","sources":["Proceedings of the 40th International Conference on Machine Learning","Proceedings of the Machine Learning 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