Multi-Group Proportional Representation in Retrieval

Alex Oesterling, Claudio Mayrink Verdun, Alexander Glynn, Carol Xuan Long, Lucas Monteiro Paes, Sajani Vithana, Martina Cardone, Flavio Calmon

Keywords: Fairness, Proportional Representation, Multi-Group Fairness

TL;DR: We introduce a novel metric for ensuring multi-group proportional representation over sets of images. We apply this metric to retrieval and propose an algorithm that maximizes similarity under a multi-group proportional representation constraint.

Abstract:

Image search and retrieval tasks can perpetuate harmful stereotypes, erase cultural identities, and amplify social disparities. Current approaches to mitigate these representational harms balance the number of retrieved items across population groups defined by a small number of (often binary) attributes. However, most existing methods overlook intersectional groups determined by combinations of
group attributes, such as gender, race, and ethnicity. We introduce Multi-Group Proportional Representation (MPR), a novel metric that measures representation across intersectional groups. We develop practical methods for estimating MPR, provide theoretical guarantees, and propose optimization algorithms to ensure MPR in retrieval. We demonstrate that existing methods optimizing for equal and proportional representation metrics may fail to promote MPR. Crucially, our work shows that optimizing MPR yields more proportional representation across multiple intersectional groups specified by a rich function class, often with minimal compromise in retrieval accuracy. Code is provided at https://github.com/alex-oesterling/multigroup-proportional-representation.

Primary Area: Fairness

Flagged For Ethics Review: true

Submission Number: 21620