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Models and Mechanisms for Spatial Data Fairness.

Sina Shaham1, Gabriel Ghinita2, Cyrus Shahabi1

  • 1Viterbi School of Engineering University of Southern California Los Angeles, California, USA.

Proceedings of the VLDB Endowment. International Conference on Very Large Data Bases
|May 23, 2023
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Summary
This summary is machine-generated.

This study introduces spatial data fairness to ensure equitable outcomes in location-based decisions. Novel fair polynomial mechanisms address fairness challenges unique to location data, maintaining utility.

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Area of Science:

  • Computer Science
  • Data Science
  • Geographic Information Science

Background:

  • Data-driven decisions can lead to unfair treatment for certain groups.
  • Location data, often used in decision-making, can correlate with sensitive attributes.
  • Existing fairness research has largely overlooked the unique challenges of location data.

Purpose of the Study:

  • To introduce and define the concept of spatial data fairness.
  • To address the specific fairness challenges posed by location data and spatial queries.
  • To develop mechanisms for achieving fairness in location-based decision-making.

Main Methods:

  • Introduced the concept of "fair polynomials" as a building block for fairness.
  • Proposed two novel mechanisms based on fair polynomials for spatial fairness.
  • Evaluated mechanisms on real-world data for distance-based and zone-based decisions.

Main Results:

  • The proposed mechanisms effectively achieve individual spatial fairness.
  • Fairness was achieved without significant sacrifice of data utility.
  • Experimental results demonstrate the viability of the approach on real data.

Conclusions:

  • Spatial data fairness is crucial for equitable location-based decision-making.
  • Fair polynomials offer a promising approach to address fairness in spatial data.
  • The developed mechanisms provide practical solutions for fair location-based services.