Fair Spatial Indexing: A paradigm for Group Spatial Fairness.

Sina Shaham, Gabriel Ghinita, Cyrus Shahabi
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引用次数: 0

Abstract

Machine learning (ML) is playing an increasing role in decision-making tasks that directly affect individuals, e.g., loan approvals, or job applicant screening. Significant concerns arise that, without special provisions, individuals from under-privileged backgrounds may not get equitable access to services and opportunities. Existing research studies fairness with respect to protected attributes such as gender, race or income, but the impact of location data on fairness has been largely overlooked. With the widespread adoption of mobile apps, geospatial attributes are increasingly used in ML, and their potential to introduce unfair bias is significant, given their high correlation with protected attributes. We propose techniques to mitigate location bias in machine learning. Specifically, we consider the issue of miscalibration when dealing with geospatial attributes. We focus on spatial group fairness and we propose a spatial indexing algorithm that accounts for fairness. Our KD-tree inspired approach significantly improves fairness while maintaining high learning accuracy, as shown by extensive experimental results on real data.

公平空间索引:群体空间公平范例
机器学习(ML)在直接影响个人的决策任务(如贷款审批或求职者筛选)中发挥着越来越重要的作用。如果没有特殊规定,来自弱势背景的个人可能无法公平地获得服务和机会,这引起了人们的极大关注。现有研究对性别、种族或收入等受保护属性的公平性进行了研究,但位置数据对公平性的影响在很大程度上被忽视了。随着移动应用程序的广泛采用,地理空间属性越来越多地用于 ML,鉴于其与受保护属性的高度相关性,它们引入不公平偏见的可能性非常大。我们提出了在机器学习中减轻位置偏差的技术。具体来说,我们考虑了处理地理空间属性时的误判问题。我们将重点放在空间组公平性上,并提出了一种考虑公平性的空间索引算法。我们的 KD 树启发方法在保持高学习准确性的同时,显著提高了公平性,这一点已通过在真实数据上的大量实验结果得到证明。
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