Models and Mechanisms for Spatial Data Fairness

TL;DR

This paper introduces the concept of spatial data fairness, proposing novel mechanisms using fair polynomials to ensure fairness in location-based decision-making without losing utility.

Contribution

It presents the first formal framework for spatial data fairness and develops mechanisms that achieve fairness in location-based decisions.

Findings

Mechanisms achieve spatial fairness effectively.

Fairness is maintained without utility loss.

Applicable to distance-based and zone-based decisions.

Abstract

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.