2026-07-23T13:24:36Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:13875 2024-03-06T10:53:04Z ddc:004 open_access

Causal Intersectionality and Fair Ranking Yang, Ke Loftus, Joshua R. Stoyanovich, Julia fairness intersectionality ranking causality 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Ke Yang and Joshua R. Loftus and Julia Stoyanovich 2021 Is Part Of LIPIcs, Volume 192, 2nd Symposium on Foundations of Responsible Computing (FORC 2021) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.FORC.2021.7 urn:nbn:de:0030-drops-138756 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2021.7 eng https://creativecommons.org/licenses/by/4.0/legalcode