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Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
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FairSight: Visual Analytics for Fairness in Decision Making.

Yongsu Ahn, Yu-Ru Lin

    IEEE Transactions on Visualization and Computer Graphics
    |August 20, 2019
    PubMed
    Summary
    This summary is machine-generated.

    FairSight is a new visual analytic system designed to promote fairness in data-driven decision-making. It helps users understand, measure, diagnose, and mitigate biases for fairer outcomes.

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    Area of Science:

    • Computer Science
    • Human-Computer Interaction
    • Data Science

    Background:

    • Data-driven decision-making is widespread but raises concerns about potential discrimination.
    • Existing fairness measures and algorithms are not widely adopted in practice.
    • There is a critical need for practical tools to ensure fairness in automated decisions.

    Purpose of the Study:

    • To introduce FairSight, a visual analytic system for facilitating fair data-driven decision-making.
    • To enable users to understand, measure, diagnose, and mitigate biases in decision pipelines.
    • To improve fairness in ranking decisions.

    Main Methods:

    • Development of FairSight, a visual analytic system.
    • Integration of modules for understanding, measuring, diagnosing, and mitigating biases.
    • Evaluation through a case study and a user study.

    Main Results:

    • The proposed visual analytic and diagnostic modules effectively address fairness concerns.
    • FairSight facilitates a better understanding of the fairness-aware decision pipeline.
    • The system contributes to achieving fairer decision-making outcomes.

    Conclusions:

    • FairSight provides a viable tool for implementing fairness in real-world data-driven decision-making.
    • Visual analytics are effective in promoting fairness and mitigating bias.
    • The system supports multiple notions of fairness in ranking decisions.