Spatial Fairness: The Case for its Importance, Limitations of Existing Work, and Guidelines for Future Research
<|reference_start|>Spatial Fairness: The Case for its Importance, Limitations of Existing Work, and Guidelines for Future Research: Despite location being increasingly used in decision-making systems employed in many sensitive domains such as mortgages and insurance, astonishingly little attention has been paid to 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}, by outlining the harms that continue to be perpetuated due to location's correlation with protected characteristics. This interdisciplinary work connects knowledge from fields such as public policy, economic development, and geography to highlight how fair-AI research currently falls short of correcting for spatial biases, and does not consider challenges unique to spatial data. Furthermore, we identify limitations of the handful of spatial fairness work proposed so far, and finally, detail guidelines for future research so subsequent work may avoid such issues and help correct spatial biases.<|reference_end|>
@article{saxena2024spatial,
    title={Spatial Fairness: The Case for its Importance, Limitations of Existing
  Work, and Guidelines for Future Research},
    author={Nripsuta Ani Saxena, Wenbin Zhang, Cyrus Shahabi},
    journal={arXiv preprint arXiv:2403.14040},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14040},
    primaryClass={cs.CY}
}