Causal intersectionality for fair ranking

TL;DR

This paper introduces a causal modeling framework for intersectional fairness in ranking systems, enabling more transparent and domain-aware fair ranking methods with experimental validation on real and synthetic data.

Contribution

It presents a novel causal approach to intersectional fairness in ranking, addressing a gap in causal inference and fairness literature.

Findings

Effective in modeling intersectional fairness

Demonstrates flexibility across different tasks

Reveals limitations under certain assumptions

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 results 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 fair machine learning 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 behaviour under different structural assumptions.