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....
Additional URL: https://www.researchgate.net/publication/364299993...
URN: urn:nbn:de:bsz:180-madoc-631116
Document Type: Article
Year of publication: 2022
The title of a journal, publication series: Frontiers in Sociology
Volume: 7
Issue number: Article 883999
Page range: 1-18
Place of publication: Lausanne
Publishing house: Frontiers Media
ISSN: 2297-7775
Publication language: English
Institution: School of Social Sciences > Statistik u. Sozialwissenschaftliche Methodenlehre (Kreuter 2014-2020)
Pre-existing license: Creative Commons Attribution 4.0 International (CC BY 4.0)
Subject: 300 Social sciences, sociology, anthropology
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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