  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prominent institutions favoring outdated cultural norms and legacy communities.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.
