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From fair predictions to just decisions? Conceptualizing algorithmic fairness and distributive justice in the context of data-driven decision-making


Kuppler, Matthias ; Kern, Christoph ; Bach, Ruben L. ; Kreuter, Frauke


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DOI: https://doi.org/10.3389/fsoc.2022.883999
URL: https://www.frontiersin.org/articles/10.3389/fsoc....
Weitere URL: https://www.researchgate.net/publication/364299993...
URN: urn:nbn:de:bsz:180-madoc-631116
Dokumenttyp: Zeitschriftenartikel
Erscheinungsjahr: 2022
Titel einer Zeitschrift oder einer Reihe: Frontiers in Sociology
Band/Volume: 7
Heft/Issue: Article 883999
Seitenbereich: 1-18
Ort der Veröffentlichung: Lausanne
Verlag: Frontiers Media
ISSN: 2297-7775
Sprache der Veröffentlichung: Englisch
Einrichtung: Fakultät für Sozialwissenschaften > Statistik u. Sozialwissenschaftliche Methodenlehre (Kreuter 2014-2020)
Bereits vorhandene Lizenz: Creative Commons Namensnennung 4.0 International (CC BY 4.0)
Fachgebiet: 300 Sozialwissenschaften, Soziologie, Anthropologie
Abstract: Prediction algorithms are regularly used to support and automate high-stakes policy decisions about the allocation of scarce public resources. However, data-driven decision-making raises problems of algorithmic fairness and justice. So far, fairness and justice are frequently conflated, with the consequence that distributive justice concerns are not addressed explicitly. In this paper, we approach this issue by distinguishing (a) fairness as a property of the algorithm used for the prediction task from (b) justice as a property of the allocation principle used for the decision task in data-driven decision-making. The distinction highlights the different logic underlying concerns about fairness and justice and permits a more systematic investigation of the interrelations between the two concepts. We propose a new notion of algorithmic fairness called error fairness which requires prediction errors to not differ systematically across individuals. Drawing on sociological and philosophical discourse on local justice, we present a principled way to include distributive justice concerns into data-driven decision-making. We propose that allocation principles are just if they adhere to well-justified distributive justice principles. Moving beyond the one-sided focus on algorithmic fairness, we thereby make a first step toward the explicit implementation of distributive justice into data-driven decision-making.




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BASE: Kuppler, Matthias ; Kern, Christoph ; Bach, Ruben L. ; Kreuter, Frauke

Google Scholar: Kuppler, Matthias ; Kern, Christoph ; Bach, Ruben L. ; Kreuter, Frauke

ORCID: Kuppler, Matthias ; Kern, Christoph ORCID: 0000-0001-7363-4299 ; Bach, Ruben L. ORCID: 0000-0001-5690-2829 ; Kreuter, Frauke ORCID: 0000-0002-7339-2645

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