Enforcing group fairness in algorithmic decision making : utility maximization under sufficiency

Baumann, Joachim; Hannák, Anikó; Heitz, Christoph

doi:10.1145/3531146.3534645

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-25909

Full metadata record

DC Field	Value	Language
dc.contributor.author	Baumann, Joachim	-
dc.contributor.author	Hannák, Anikó	-
dc.contributor.author	Heitz, Christoph	-
dc.date.accessioned	2022-11-03T15:39:04Z	-
dc.date.available	2022-11-03T15:39:04Z	-
dc.date.issued	2022	-
dc.identifier.isbn	978-1-4503-9352-2	de_CH
dc.identifier.uri	https://digitalcollection.zhaw.ch/handle/11475/25909	-
dc.description.abstract	Binary decision making classifiers are not fair by default. Fairness requirements are an additional element to the decision making rationale, which is typically driven by maximizing some utility function. In that sense, algorithmic fairness can be formulated as a constrained optimization problem. This paper contributes to the discussion on how to implement fairness, focusing on the fairness concepts of positive predictive value (PPV) parity, false omission rate (FOR) parity, and sufficiency (which combines the former two). We show that group-specific threshold rules are optimal for PPV parity and FOR parity, similar to well-known results for other group fairness criteria. However, depending on the underlying population distributions and the utility function, we find that sometimes an upper-bound threshold rule for one group is optimal: utility maximization under PPV parity (or FOR parity) might thus lead to selecting the individuals with the smallest utility for one group, instead of selecting the most promising individuals. This result is counter-intuitive and in contrast to the analogous solutions for statistical parity and equality of opportunity. We also provide a solution for the optimal decision rules satisfying the fairness constraint sufficiency. We show that more complex decision rules are required and that this leads to within-group unfairness for all but one of the groups. We illustrate our findings based on simulated and real data.	de_CH
dc.language.iso	en	de_CH
dc.publisher	Association for Computing Machinery	de_CH
dc.rights	Licence according to publishing contract	de_CH
dc.subject	Computing methodology	de_CH
dc.subject	Machine learning	de_CH
dc.subject	Applied computing	de_CH
dc.subject	Decision analysis	de_CH
dc.subject	Group fairness metrics	de_CH
dc.subject	Algorithmic fairness	de_CH
dc.subject	Prediction-based decision making	de_CH
dc.subject.ddc	005: Computerprogrammierung, Programme und Daten	de_CH
dc.subject.ddc	658.403: Entscheidungsfindung, Informationsmanagement	de_CH
dc.title	Enforcing group fairness in algorithmic decision making : utility maximization under sufficiency	de_CH
dc.type	Konferenz: Paper	de_CH
dcterms.type	Text	de_CH
zhaw.departement	School of Engineering	de_CH
zhaw.organisationalunit	Institut für Datenanalyse und Prozessdesign (IDP)	de_CH
zhaw.publisher.place	New York	de_CH
dc.identifier.doi	10.1145/3531146.3534645	de_CH
dc.identifier.doi	10.21256/zhaw-25909	-
zhaw.conference.details	5th ACM Conference on Fairness, Accountability, and Transparency (FAccT), Seoul, Republic of Korea, 21-24 June 2022	de_CH
zhaw.funding.eu	No	de_CH
zhaw.originated.zhaw	Yes	de_CH
zhaw.pages.end	2326	de_CH
zhaw.pages.start	2315	de_CH
zhaw.publication.status	acceptedVersion	de_CH
zhaw.publication.review	Peer review (Publikation)	de_CH
zhaw.title.proceedings	Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency	de_CH
zhaw.funding.snf	187473	de_CH
zhaw.funding.zhaw	Algorithmic Fairness in data-based decision making: Combining ethics and technology	de_CH
zhaw.author.additional	No	de_CH
zhaw.display.portrait	Yes	de_CH
Appears in collections:	Publikationen School of Engineering

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Baumann, J., Hannák, A., & Heitz, C. (2022). Enforcing group fairness in algorithmic decision making : utility maximization under sufficiency [Conference paper]. Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 2315–2326. https://doi.org/10.1145/3531146.3534645

Baumann, J., Hannák, A. and Heitz, C. (2022) ‘Enforcing group fairness in algorithmic decision making : utility maximization under sufficiency’, in Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. New York: Association for Computing Machinery, pp. 2315–2326. Available at: https://doi.org/10.1145/3531146.3534645.

J. Baumann, A. Hannák, and C. Heitz, “Enforcing group fairness in algorithmic decision making : utility maximization under sufficiency,” in Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 2022, pp. 2315–2326. doi: 10.1145/3531146.3534645.

BAUMANN, Joachim, Anikó HANNÁK und Christoph HEITZ, 2022. Enforcing group fairness in algorithmic decision making : utility maximization under sufficiency. In: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. Conference paper. New York: Association for Computing Machinery. 2022. S. 2315–2326. ISBN 978-1-4503-9352-2

Baumann, Joachim, Anikó Hannák, and Christoph Heitz. 2022. “Enforcing Group Fairness in Algorithmic Decision Making : Utility Maximization under Sufficiency.” Conference paper. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 2315–26. New York: Association for Computing Machinery. https://doi.org/10.1145/3531146.3534645.

Baumann, Joachim, et al. “Enforcing Group Fairness in Algorithmic Decision Making : Utility Maximization under Sufficiency.” Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, Association for Computing Machinery, 2022, pp. 2315–26, https://doi.org/10.1145/3531146.3534645.