  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.
