Causal intersectionality for fair ranking
This addresses fairness issues in rankings for applications like web search and admissions, but is incremental as it builds on existing causal fairness methods.
The paper tackles the problem of achieving intersectional fairness in rankings by proposing a causal modeling approach, and demonstrates its behavior on real and synthetic datasets under various structural assumptions.
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.