Despite location being increasingly used in decision-making systems deployed in sensitive domains such as mortgages and insurance, little attention has been paid to the unfairness that may seep in due to the correlation of location with characteristics considered protected under anti-discrimination law, such as race or national origin. This position paper argues for the urgent need to consider fairness with respect to location, termed $\textit{spatial fairness}$. It outlines the harms perpetuated through location's correlation with protected characteristics, which may be particularly consequential due to its treatment as a neutral or purely technical attribute, abstracted from its historical, political, and socioeconomic context. This interdisciplinary work connects knowledge from fields such as public policy, economic development, and geography to highlight how existing fair-AI research falls short in addressing spatial biases, and fails to consider challenges unique to spatial data. Furthermore, we identify limitations in the small body of prior work on spatial fairness work, and propose guidelines to inform future research aimed at mitigating spatial biases in data-driven decision-making systems.

翻译：尽管地理位置在抵押贷款和保险等敏感领域的决策系统中得到日益广泛的应用，但人们很少关注由于地理位置与反歧视法所保护特征（如种族或国籍）之间的相关性而可能渗入的不公平现象。本立场文件论证了考虑地理位置相关公平性（称为$\textit{空间公平性}$）的迫切必要性。文章阐述了通过地理位置与受保护特征之间的相关性所延续的损害，这种损害可能因其被视作中立的或纯粹技术性的属性而显得尤为严重——这种认知将其从历史、政治和社会经济背景中抽离出来。这项跨学科研究融合了公共政策、经济发展和地理学等领域的知识，强调现有公平人工智能研究在应对空间偏见方面的不足，以及未能考虑空间数据特有挑战的问题。此外，我们指出了先前少量空间公平性研究存在的局限性，并提出了指导未来研究的准则，旨在缓解数据驱动决策系统中的空间偏见。