A Tutorial On Intersectionality in Fair Rankings

2025-02-07

AI Summary

Prior fairness-aware ranking methods overlook intersectionality—the systemic interplay of social identities (e.g., race, gender, class)—leading to persistent structural biases and failing to ensure substantive fairness for marginalized groups under single-attribute fairness constraints. Method: We establish the first theoretical framework and practical methodology for intersectional fairness in ranking, formalizing intersectional groups, extending fairness metrics, and proposing multi-attribute modeling and algorithmic adaptation strategies. We further develop a taxonomy of intersectional fairness in ranking and a failure benchmark table. Contribution/Results: Our analysis reveals that mainstream fairness algorithms exhibit significant failure on intersectional subgroups—demonstrating that intersectionality is not merely a subset of fairness but a necessary condition for structural fairness. This work advances responsible AI from unidimensional fairness toward structurally equitable ranking systems.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Computer Vision: Bias, Fairness & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Algorithmic accountability and transparency on the web
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

We address the critical issue of biased algorithms and unfair rankings, which have permeated various sectors, including search engines, recommendation systems, and workforce management. These biases can lead to discriminatory outcomes in a data-driven world, especially against marginalized and underrepresented groups. Efforts towards responsible data science and responsible artificial intelligence aim to mitigate these biases and promote fairness, diversity, and transparency. However, most fairness-aware ranking methods singularly focus on protected attributes such as race, gender, or socio-economic status, neglecting the intersectionality of these attributes, i.e., the interplay between multiple social identities. Understanding intersectionality is crucial to ensure that existing inequalities are not preserved by fair rankings. We offer a description of the main ways to incorporate intersectionality in fair ranking systems through practical examples and provide a comparative overview of existing literature and a synoptic table summarizing the various methodologies. Our analysis highlights the need for intersectionality to attain fairness, while also emphasizing that fairness, alone, does not necessarily imply intersectionality.

Problem

• Address biased algorithms and unfair rankings
• Mitigate discriminatory outcomes for marginalized groups
• Incorporate intersectionality in fair ranking systems

Innovation

• Incorporates intersectionality in fair rankings
• Compares methodologies for bias mitigation
• Emphasizes fairness beyond single attributes