Fair Spatial Indexing: A paradigm for Group Spatial Fairness

Sina Shaham, Gabriel Ghinita, Cyrus Shahabi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationAdvances in Database Technology - EDBT
PublisherOpenProceedings.org
Pages150-161
Number of pages12
Edition2
ISBN (Electronic)9783893180912, 9783893180943, 9783893180950
DOIs
Publication statusPublished - 22 Nov 2023
Event27th International Conference on Extending Database Technology, EDBT 2024 - Paestum, Italy
Duration: 25 Mar 202428 Mar 2024

Publication series

NameAdvances in Database Technology - EDBT
Number2
Volume27
ISSN (Electronic)2367-2005

Conference

Conference27th International Conference on Extending Database Technology, EDBT 2024
Country/TerritoryItaly
CityPaestum
Period25/03/2428/03/24

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