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Publication type: Conference paper
Type of review: Peer review (publication)
Title: On the moral justification of statistical parity
Authors: Hertweck, Corinna
Heitz, Christoph
Loi, Michele
et. al: No
DOI: 10.1145/3442188.3445936
Proceedings: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
Pages: 747
Pages to: 757
Conference details: FAccT '21: 2021 ACM Conference on Fairness, Accountability, and Transparency, Virtual Event, Canada, 3-10 March 2021
Issue Date: Mar-2021
Publisher / Ed. Institution: Association for Computing Machinery
Publisher / Ed. Institution: New York
ISBN: 978-1-4503-8309-7
Language: English
Subjects: Fairness; Independence; Statistical parity; Distributive justice; Bias
Subject (DDC): 005: Computer programming, programs and data
170: Ethics
Abstract: A crucial but often neglected aspect of algorithmic fairness is the question of how we justify enforcing a certain fairness metric from a moral perspective. When fairness metrics are proposed, they are typically argued for by highlighting their mathematical properties. Rarely are the moral assumptions beneath the metric explained. Our aim in this paper is to consider the moral aspects associated with the statistical fairness criterion of independence (statistical parity). To this end, we consider previous work, which discusses the two worldviews "What You See Is What You Get" (WYSIWYG) and "We're All Equal" (WAE) and by doing so provides some guidance for clarifying the possible assumptions in the design of algorithms. We present an extension of this work, which centers on morality. The most natural moral extension is that independence needs to be fulfilled if and only if differences in predictive features (e.g. high school grades and standardized test scores are predictive of performance at university) between socio-demographic groups are caused by unjust social disparities or measurement errors. Through two counterexamples, we demonstrate that this extension is not universally true. This means that the question of whether independence should be used or not cannot be satisfactorily answered by only considering the justness of differences in the predictive features.
Further description: The final publication is available in the ACM Digital Library via
Fulltext version: Accepted version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Appears in collections:Publikationen School of Engineering

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