MANIRank: Multiple Attribute and Intersectional Fairness for Consensus Ranking

Cachel, Kathleen

Etd

MANIRank: Multiple Attribute and Intersectional Fairness for Consensus Ranking

Public

Combining the preferences of many rankers into one single consensus ranking is critical for consequential applications from hiring and admissions to lending. While group fairness has been extensively studied for classification, group fairness in rankings and in particular rank aggregation remains in its infancy. Recent work introduced the concept of fair rank aggregation for combining rankings but restricted to the case when candidates have a single binary protected attribute, i.e., they fall into two groups only. Yet it remains an open problem how to create a consensus ranking that represents the preferences of all rankers while ensuring fair treatment for candidates with multiple protected attributes such as gender, race, and nationality. In this work, I am the first to define and solve this open Multi-attribute Fair Consensus Ranking (MFCR) problem. As a foundation, I design novel group fairness criteria for rankings, called MANI-Rank, ensuring fair treatment of groups defined by individual protected attributes and their intersection. Leveraging the MANI-Rank criteria, I develop a series of algorithms that for the first time tackle the MFCR problem. My experimental study with a rich variety of consensus scenarios demonstrates my MFCR methodology is the only approach to achieve both intersectional and protected attribute fairness while also representing the preferences expressed through many base rankings. The real-world case study on merit scholarships illustrates the effectiveness of my MFCR methods to mitigate bias across multiple protected attributes and their intersections.

Creator