Models and Mechanisms for Spatial Data Fairness

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.