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Causal Intersectionality and Fair Ranking

Authors Ke Yang, Joshua R. Loftus, Julia Stoyanovich

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Author Details

Ke Yang
  • New York University, NY, USA
Joshua R. Loftus
  • London School of Economics, UK
Julia Stoyanovich
  • New York University, NY, USA

Funding

This work was supported in part by National Science Foundation (NSF) Grants No. 1926250, 1934464, and 1916505.

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Ke Yang, Joshua R. Loftus, and Julia Stoyanovich. Causal Intersectionality and Fair Ranking. In 2nd Symposium on Foundations of Responsible Computing (FORC 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 192, pp. 7:1-7:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/LIPIcs.FORC.2021.7

Abstract

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.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Ranking
Keywords
  • fairness
  • intersectionality
  • ranking
  • causality

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