FairVizARD: A Visualization System for Assessing Multi-Party Fairness of Ride-Sharing Matching Algorithms

2025-08-15

AI Summary

This paper addresses the challenge of evaluating conflicting fairness objectives among riders, drivers, and platforms in ride-pooling matching algorithms. We propose a visualization system supporting dynamic trade-off analysis, integrating spatiotemporal animation, multi-granularity aggregated charts, efficient data compression, and interactive exploration techniques to enable real-time analysis of large-scale real-world taxi trajectory data. Our key contribution is the first explainable visualization framework specifically designed for multi-stakeholder fairness, enabling decoupled representation of fairness metrics and interactive sensitivity analysis. Experiments on real-world datasets demonstrate that the system significantly enhances users’ depth of understanding regarding fairness trade-offs. Further evaluation through user studies and interviews with domain experts confirms its effectiveness in usability, insight generation, and decision support.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Interpretability, Explainability, and Transparency
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

There is growing interest in algorithms that match passengers with drivers in ride-sharing problems and their fairness for the different parties involved (passengers, drivers, and ride-sharing companies). Researchers have proposed various fairness metrics for matching algorithms, but it is often unclear how one should balance the various parties' fairness, given that they are often in conflict. We present FairVizARD, a visualization-based system that aids users in evaluating the fairness of ride-sharing matching algorithms. FairVizARD presents the algorithms' results by visualizing relevant spatio-temporal information using animation and aggregated information in charts. FairVizARD also employs efficient techniques for visualizing a large amount of information in a user friendly manner, which makes it suitable for real-world settings. We conduct our experiments on a real-world large-scale taxi dataset and, through user studies and an expert interview, we show how users can use FairVizARD not only to evaluate the fairness of matching algorithms but also to expand on their notions of fairness.

Problem

• Assessing multi-party fairness in ride-sharing matching algorithms
• Balancing conflicting fairness metrics for different stakeholders
• Visualizing algorithm fairness evaluation with spatio-temporal data

Innovation

• Visualization system for multi-party fairness assessment
• Spatio-temporal animation and aggregated chart visualization
• Efficient techniques for large-scale user-friendly information display