The placement of art in public spaces can have a significant impact on who feels a sense of belonging. In cities, public art communicates whose interests and culture are being favored. In this paper, we propose a graph matching approach with local constraints to build a curatorial tool for selecting public art in a way that supports inclusive spaces. We develop a cost matrix by drawing on Schelling's model of segregation. Using the cost matrix as an input, the optimization problem is solved via projected gradient descent to obtain a soft assignment matrix. We discuss regularization terms to set curatorial constraints. Our optimization program allocates artwork to public spaces and walls 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. Using Tufts University as a testbed, we assess the effectiveness of our approach and discuss its potential pitfalls from both a curatorial and equity standpoint.
翻译:在公共空间放置艺术可以对归属感的归属感产生重大影响。 在城市,公共艺术可以传达其利益和文化得到偏好的公共艺术。 在本文中,我们提出一个图表,与地方制约因素匹配,以构建一种支持包容性空间的方式选择公共艺术的法庭工具。我们通过利用舍灵的隔离模式开发一个成本矩阵。利用成本矩阵作为一种投入,优化问题通过预测的梯度下降获得软性分配矩阵解决。我们讨论了制定法律约束的正规化条件。我们优化方案通过满足最低代表性和暴露标准,将艺术作品分配到公共空间和墙壁上,从而将“集体”偏好非优先化。我们利用现有文献为我们的算法产出制定公平衡量标准。我们用塔夫特大学作为测试点,评估我们的方法的有效性,并从法庭和公平的角度讨论其潜在的陷阱。