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and context, and yet, recent work has demonstrated numerous instances of art in prominent institutions favoring outdated cultural norms and legacy communities.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.

翻译：公共艺术塑造了我们的共享空间。公共艺术应当反映社区与语境，然而近期研究表明，众多重要机构中的艺术作品仍倾向于推崇过时的文化规范和传统社区。基于此，我们开发了一种新型推荐系统，通过内置公平目标与基于局部价值约束的资源分配机制来策展公共艺术作品。借鉴谢林隔离模型，我们构建了成本矩阵，并以此作为输入，通过投影梯度下降法优化评分函数，得到软分配矩阵。我们的优化方案在分配艺术品至公共空间时，通过满足最低表征与曝光标准来弱化"圈内"偏好。我们依托现有文献为算法输出构建了公平性度量指标，评估了该方法的效果，并从策展与公平双重视角探讨了其潜在风险。