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  2. School of Engineering
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  4. Publikationen School of Engineering
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dc.contributor.authorHertweck, Corinna-
dc.contributor.authorRäz, Tim-
dc.date.accessioned2023-12-15T08:46:40Z-
dc.date.available2023-12-15T08:46:40Z-
dc.date.issued2022-02-25-
dc.identifier.isbn978-1-57735-876-3de_CH
dc.identifier.issn2374-3468de_CH
dc.identifier.issn2159-5399de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/29379-
dc.descriptionAAM of the article with technical appendix can be found here: https://arxiv.org/abs/2109.04399de_CH
dc.description.abstractImpossibility results show that important fairness measures (independence, separation, sufficiency) cannot be satisfied at the same time under reasonable assumptions. This paper explores whether we can satisfy and/or improve these fairness measures simultaneously to a certain degree. We introduce information-theoretic formulations of the fairness measures and define degrees of fairness based on these formulations. The information-theoretic formulations suggest unexplored theoretical relations between the three fairness measures. In the experimental part, we use the information-theoretic expressions as regularizers to obtain fairness-regularized predictors for three standard datasets. Our experiments show that a) fairness regularization directly increases fairness measures, in line with existing work, and b) some fairness regularizations indirectly increase other fairness measures, as suggested by our theoretical findings. This establishes that it is possible to increase the degree to which some fairness measures are satisfied at the same time -- some fairness measures are gradually compatible.de_CH
dc.language.isoende_CH
dc.publisherAssociation for the Advancement of Artificial Intelligencede_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectFairnessde_CH
dc.subjectDiscriminationde_CH
dc.subjectStatistical parityde_CH
dc.subjectAccuracyde_CH
dc.subjectMutual informationde_CH
dc.subjectEntropyde_CH
dc.subjectSeparationde_CH
dc.subjectSufficiencyde_CH
dc.subjectIndependencede_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc170: Ethikde_CH
dc.titleGradual (in)compatibility of fairness criteriade_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
zhaw.publisher.placePalo Alto, CAde_CH
dc.identifier.doi10.1609/aaai.v36i11.21450de_CH
zhaw.conference.details36th AAAI Conference on Artificial Intelligence, online, 22 February–1 March 2022de_CH
zhaw.funding.euNode_CH
zhaw.issue11de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end11934de_CH
zhaw.pages.start11926de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume36de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsProceedings of the 36th AAAI Conference on Artificial Intelligence : Vol. 36 No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrationsde_CH
zhaw.funding.snf187473de_CH
zhaw.funding.zhawSocially acceptable AI and fairness trade-offs in predictive analyticsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Hertweck, C., & Räz, T. (2022). Gradual (in)compatibility of fairness criteria [Conference paper]. Proceedings of the 36th AAAI Conference on Artificial Intelligence : Vol. 36 No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations, 36(11), 11926–11934. https://doi.org/10.1609/aaai.v36i11.21450
Hertweck, C. and Räz, T. (2022) ‘Gradual (in)compatibility of fairness criteria’, in Proceedings of the 36th AAAI Conference on Artificial Intelligence : Vol. 36 No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations. Palo Alto, CA: Association for the Advancement of Artificial Intelligence, pp. 11926–11934. Available at: https://doi.org/10.1609/aaai.v36i11.21450.
C. Hertweck and T. Räz, “Gradual (in)compatibility of fairness criteria,” in Proceedings of the 36th AAAI Conference on Artificial Intelligence : Vol. 36 No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations, Feb. 2022, vol. 36, no. 11, pp. 11926–11934. doi: 10.1609/aaai.v36i11.21450.
HERTWECK, Corinna und Tim RÄZ, 2022. Gradual (in)compatibility of fairness criteria. In: Proceedings of the 36th AAAI Conference on Artificial Intelligence : Vol. 36 No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations. Conference paper. Palo Alto, CA: Association for the Advancement of Artificial Intelligence. 25 Februar 2022. S. 11926–11934. ISBN 978-1-57735-876-3
Hertweck, Corinna, and Tim Räz. 2022. “Gradual (in)Compatibility of Fairness Criteria.” Conference paper. In Proceedings of the 36th AAAI Conference on Artificial Intelligence : Vol. 36 No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations, 36:11926–34. Palo Alto, CA: Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v36i11.21450.
Hertweck, Corinna, and Tim Räz. “Gradual (in)Compatibility of Fairness Criteria.” Proceedings of the 36th AAAI Conference on Artificial Intelligence : Vol. 36 No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations, vol. 36, no. 11, Association for the Advancement of Artificial Intelligence, 2022, pp. 11926–34, https://doi.org/10.1609/aaai.v36i11.21450.


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