Distributive justice and fairness metrics in automated decision-making: How much overlap is there?

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

URL: https://arxiv.org/abs/2105.01441
Additional URL: https://transfer-learning.ai/paper/distributive-ju...
Document Type: Working paper
Year of publication: 2021
Place of publication: Ithaca, NY
Publishing house: Cornell University
Publication language: English
Institution: School of Social Sciences > Statistik u. Sozialwissenschaftliche Methodenlehre (Kreuter 2014-2020)
Subject: 300 Social sciences, sociology, anthropology
Abstract: The advent of powerful prediction algorithms led to increased automation of high-stake decisions regarding the allocation of scarce resources such as government spending and welfare support. This automation bears the risk of perpetuating unwanted discrimination against vulnerable and historically disadvantaged groups. Research on algorithmic discrimination in computer science and other disciplines developed a plethora of fairness metrics to detect and correct discriminatory algorithms. Drawing on robust sociological and philosophical discourse on distributive justice, we identify the limitations and problematic implications of prominent fairness metrics. We show that metrics implementing equality of opportunity only apply when resource allocations are based on deservingness, but fail when allocations should reflect concerns about egalitarianism, sufficiency, and priority. We argue that by cleanly distinguishing between prediction tasks and decision tasks, research on fair machine learning could take better advantage of the rich literature on distributive justice.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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