TY - GEN
T1 - Fair Spatial Indexing
T2 - 27th International Conference on Extending Database Technology, EDBT 2024
AU - Shaham, Sina
AU - Ghinita, Gabriel
AU - Shahabi, Cyrus
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s).
PY - 2023/11/22
Y1 - 2023/11/22
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85190975178&partnerID=8YFLogxK
U2 - 10.48786/edbt.2024.14
DO - 10.48786/edbt.2024.14
M3 - Conference contribution
AN - SCOPUS:85190975178
T3 - Advances in Database Technology - EDBT
SP - 150
EP - 161
BT - Advances in Database Technology - EDBT
PB - OpenProceedings.org
Y2 - 25 March 2024 through 28 March 2024
ER -