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Computer Science > Computers and Society

arXiv:2109.04230 (cs)
[Submitted on 9 Sep 2021]

Title:A Systematic Approach to Group Fairness in Automated Decision Making

Authors:Corinna Hertweck, Christoph Heitz
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Abstract:While the field of algorithmic fairness has brought forth many ways to measure and improve the fairness of machine learning models, these findings are still not widely used in practice. We suspect that one reason for this is that the field of algorithmic fairness came up with a lot of definitions of fairness, which are difficult to navigate. The goal of this paper is to provide data scientists with an accessible introduction to group fairness metrics and to give some insight into the philosophical reasoning for caring about these metrics. We will do this by considering in which sense socio-demographic groups are compared for making a statement on fairness.
Comments: Accepted full paper at SDS2021, the 8th Swiss Conference on Data Science
Subjects: Computers and Society (cs.CY); Machine Learning (cs.LG)
Cite as: arXiv:2109.04230 [cs.CY]
  (or arXiv:2109.04230v1 [cs.CY] for this version)
  https://doi.org/10.48550/arXiv.2109.04230
Journal reference: In 2021 8th Swiss Conference on Data Science (SDS) (pp. 1-6). IEEE (2021)
Related DOI: https://doi.org/10.1109/SDS51136.2021.00008

Submission history

From: Corinna Hertweck [view email]
[v1] Thu, 9 Sep 2021 12:47:15 UTC (86 KB)
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