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

TL;DR

FairVizARD is a visualization system designed to help users evaluate and compare the fairness of ride-sharing matching algorithms across multiple parties using visual analytics and real-world data.

Contribution

The paper introduces FairVizARD, a novel visualization tool that enables comprehensive fairness assessment of ride-sharing algorithms, addressing multi-party fairness conflicts.

Findings

Users can effectively evaluate fairness using visualizations.

FairVizARD helps users understand multi-party fairness conflicts.

System is suitable for real-world large-scale datasets.

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.