An Equity-Aware Recommender System for Curating Art Exhibits Based on Locally-Constrained Graph Matching

TL;DR

This paper introduces an equity-aware recommender system for public art exhibits that uses a locally-constrained graph matching approach to promote diverse representation and community relevance.

Contribution

It presents a novel optimization-based method incorporating fairness and local community constraints for curating public art exhibits.

Findings

Effective allocation of artwork with minimized in-group bias

Improved diversity and representation in curated exhibits

Assessment of fairness metrics for curatorial decisions

Abstract

Public art shapes our shared spaces. Public art should speak to community and context, and yet, recent work has demonstrated numerous instances of art in prominent institutions favoring outdated cultural norms and legacy communities. Motivated by this, we develop a novel recommender system to curate public art exhibits with built-in equity objectives and a local value-based allocation of constrained resources. We develop a cost matrix by drawing on Schelling's model of segregation. Using the cost matrix as an input, the scoring function is optimized via a projected gradient descent to obtain a soft assignment matrix. Our optimization program allocates artwork to public spaces in a way that de-prioritizes "in-group" preferences, by satisfying minimum representation and exposure criteria. We draw on existing literature to develop a fairness metric for our algorithmic output, and we assess the effectiveness of our approach and discuss its potential pitfalls from both a curatorial and equity standpoint.