Spatial Fairness: The Case for its Importance, Limitations of Existing Work, and Guidelines for Future Research

TL;DR

This paper emphasizes the importance of considering spatial fairness in decision-making systems, highlighting its potential biases, limitations of current research, and proposing guidelines for future work to address spatial biases effectively.

Contribution

It introduces the concept of spatial fairness, critiques existing fair-AI approaches for neglecting spatial biases, and offers interdisciplinary guidelines for future research.

Findings

Existing fair-AI research overlooks spatial biases.

Location correlates with protected characteristics, causing unfairness.

Guidelines proposed to address spatial biases in future studies.

Abstract

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.