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Fairness in data-driven decision-making studies scenarios where individuals from certain population segments may be unfairly treated when being considered for loan or job applications, access to public resources, or other types of services. In location-based applications, decisions are based on individual whereabouts, which often correlate with sensitive attributes such as race, income, and education. While fairness has received significant attention recently, e.g., in machine learning, there is little focus on achieving fairness when dealing with location data. Due to their characteristics and specific type of processing algorithms, location data pose important fairness challenges. We introduce the concept of spatial data fairness to address the specific challenges of location data and spatial queries. We devise a novel building block to achieve fairness in the form of fair polynomials. Next, we propose two mechanisms based on fair polynomials that achieve individual spatial fairness, corresponding to two common location-based decision-making types: distance-based and zone-based. Extensive experimental results on real data show that the proposed mechanisms achieve spatial fairness without sacrificing utility.
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Fair Spatial Indexing: A paradigm for Group Spatial Fairness
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 {\em 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 {\em 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.
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- Award ID(s):
- 1909806
- PAR ID:
- 10470582
- Publisher / Repository:
- EDBT: Extending Database Technology
- Date Published:
- ISBN:
- 978-3-89318-094-3
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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