FairFuse: Interactive Visual Support for Fair Consensus Ranking
Hilson Shrestha, Kathleen Cachel, Mallak Alkhathlan, Elke, Rundensteiner, Lane Harrison

TL;DR
FairFuse is an interactive visualization tool that helps decision-makers generate, analyze, and audit fair consensus rankings considering group fairness, addressing the gap between algorithmic fairness methods and practical, user-friendly systems.
Contribution
This paper introduces FairFuse, a novel interactive visualization system that operationalizes fair consensus ranking algorithms for practical decision-making scenarios.
Findings
Supports generation of fair consensus rankings with adjustable fairness levels
Provides visual analysis tools for auditing fairness in rankings
Enhances decision-making processes with interactive fairness exploration
Abstract
Fair consensus building combines the preferences of multiple rankers into a single consensus ranking, while ensuring any group defined by a protected attribute (such as race or gender) is not disadvantaged compared to other groups. Manually generating a fair consensus ranking is time-consuming and impractical -- even for a fairly small number of candidates. While algorithmic approaches for auditing and generating fair consensus rankings have been developed, these have not been operationalized in interactive systems. To bridge this gap, we introduce FairFuse, a visualization system for generating, analyzing, and auditing fair consensus rankings. We construct a data model which includes base rankings entered by rankers, augmented with measures of group fairness, and algorithms for generating consensus rankings with varying degrees of fairness. We design novel visualizations that encode…
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Taxonomy
TopicsElectoral Systems and Political Participation
