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Causal intersectionality and fair ranking

Yang, Ke, Loftus, Joshua R. ORCID: 0000-0002-2905-1632 and Stoyanovich, Julia (2021) Causal intersectionality and fair ranking. In: Ligett, Katrina and Gupta, Swati, (eds.) 2nd Symposium on Foundations of Responsible Computing, FORC 2021. Leibniz International Proceedings in Informatics, LIPIcs. Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, Virtual, Online. ISBN 9783959771870

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Identification Number: 10.4230/LIPIcs.FORC.2021.7


In this paper we propose a causal modeling approach to intersectional fairness, and a flexible, task-specific method for computing intersectionally fair rankings. Rankings are used in many contexts, ranging from Web search to college admissions, but causal inference for fair rankings has received limited attention. Additionally, the growing literature on causal fairness has directed little attention to intersectionality. By bringing these issues together in a formal causal framework we make the application of intersectionality in algorithmic fairness explicit, connected to important real world effects and domain knowledge, and transparent about technical limitations. We experimentally evaluate our approach on real and synthetic datasets, exploring its behavior under different structural assumptions.

Item Type: Book Section
Official URL:
Additional Information: © 2021 The Authors
Divisions: Statistics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
H Social Sciences > HA Statistics
Date Deposited: 16 Jul 2021 11:57
Last Modified: 21 Jun 2024 04:48

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