论文标题
减少在社会技术系统中学习社会商品算法的歧视
Reducing Discrimination in Learning Algorithms for Social Good in Sociotechnical Systems
论文作者
论文摘要
现在，城市内的社会技术系统配备了机器学习算法，希望通过建模和预测趋势来提高效率和功能。机器学习算法已在这些领域中应用，以应对挑战，例如平衡整个城市的自行车分布以及确定乘车共享驱动程序的需求热点。但是，这些算法适用于社会技术系统中的挑战，由于以前的数据集偏见或缺乏边缘化社区的数据，使社会不平等加剧了。在本文中，我将解决城市中智能移动性计划如何使用机器学习算法来应对挑战。我还将解决这些算法如何无意间歧视社会经济地位之类的特征，以激发算法公平的重要性。使用宾夕法尼亚州匹兹堡的自行车共享计划，我将提出关于如何使用贝叶斯优化从管道中消除歧视的立场。
Sociotechnical systems within cities are now equipped with machine learning algorithms in hopes to increase efficiency and functionality by modeling and predicting trends. Machine learning algorithms have been applied in these domains to address challenges such as balancing the distribution of bikes throughout a city and identifying demand hotspots for ride sharing drivers. However, these algorithms applied to challenges in sociotechnical systems have exacerbated social inequalities due to previous bias in data sets or the lack of data from marginalized communities. In this paper, I will address how smart mobility initiatives in cities use machine learning algorithms to address challenges. I will also address how these algorithms unintentionally discriminate against features such as socioeconomic status to motivate the importance of algorithmic fairness. Using the bike sharing program in Pittsburgh, PA, I will present a position on how discrimination can be eliminated from the pipeline using Bayesian Optimization.
