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FairRankVis: A Visual Analytics Framework for Exploring Algorithmic Fairness in Graph Mining Models.

Tiankai Xie, Yuxin Ma, Jian Kang

    IEEE Transactions on Visualization and Computer Graphics
    |September 29, 2021
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    Summary
    This summary is machine-generated.

    FairRankVis is a new visual analytics framework that helps developers identify and address algorithmic bias in graph mining. It allows comparison of fairness across different algorithms and bias correction methods.

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    Area of Science:

    • Computer Science
    • Data Mining
    • Human-Computer Interaction

    Background:

    • Graph mining is crucial for recommender systems and search engines, but models often exhibit algorithmic bias.
    • Existing graph mining algorithms may perpetuate unfairness, necessitating tools for bias detection and mitigation.
    • Developers require methods to assess and correct biases in graph mining outputs.

    Purpose of the Study:

    • To introduce FairRankVis, a visual analytics framework for exploring multi-class bias in graph mining algorithms.
    • To enable developers to compare fairness across different algorithms and bias correction strategies.
    • To support the assessment of both group and individual fairness levels.

    Main Methods:

    • Development of the FairRankVis visual analytics framework.
    • Implementation of support for comparing group and individual fairness metrics.
    • Demonstration of framework utility through two usage scenarios involving algorithmic fairness inspection.

    Main Results:

    • FairRankVis facilitates the exploration of multi-class bias in graph mining.
    • The framework enables direct comparison between standard and debiased algorithms (e.g., PageRank vs. debiased PageRank).
    • Assessment of the impact of fairness-aware algorithms on both group and individual fairness is supported.

    Conclusions:

    • FairRankVis provides a valuable tool for algorithm developers to uncover and address bias in graph mining models.
    • The framework aids in understanding the trade-offs associated with employing fairness-aware algorithms.
    • Visual analytics can effectively support the development of more equitable graph mining systems.