An Equity-Aware Recommender System for Curating Art Exhibits Based on Locally-Constrained Graph Matching
This addresses equity issues in public art curation for communities, though it appears incremental as it builds on existing fairness metrics and optimization techniques.
The authors tackled the problem of biased public art curation by developing a recommender system that incorporates equity objectives and local resource constraints, resulting in a method that de-prioritizes in-group preferences and satisfies minimum representation criteria.
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and context, and yet, recent work has demonstrated numerous instances of art in prominent institutions favoring outdated cultural norms and legacy communities.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.