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Visual Analysis of Discrimination in Machine Learning.

Qianwen Wang, Zhenhua Xu, Zhutian Chen

    IEEE Transactions on Visualization and Computer Graphics
    |October 13, 2020
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    Summary
    This summary is machine-generated.

    DiscriLens is a new tool that uses visual analytics to detect and understand unfairness in automated decision-making systems. It helps identify discriminatory patterns in machine learning models.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Data Visualization

    Background:

    • Automated decision-making systems are increasingly used in critical areas like crime prediction and college admissions.
    • Concerns about fairness and potential discrimination in machine learning algorithms are growing.
    • Existing methods for assessing fairness may not provide comprehensive insights into discriminatory patterns.

    Purpose of the Study:

    • To investigate algorithmic discrimination using a visual analytics approach.
    • To introduce DiscriLens, an interactive visualization tool designed for comprehensive analysis of fairness in machine learning.
    • To support the identification and interpretation of discriminatory itemsets within automated decision-making processes.

    Main Methods:

    • Utilized causal modeling and classification rules mining to identify potentially discriminatory itemsets.
    • Developed a novel set visualization combining extended Euler diagrams and matrix-based displays.
    • Integrated interactive features for exploration and interpretation of identified discriminatory patterns.

    Main Results:

    • DiscriLens effectively reveals detailed information about algorithmic discrimination.
    • The novel set visualization facilitates quick and accurate interpretation of discriminatory itemsets.
    • User studies confirmed the usability and effectiveness of the tool in understanding fairness.

    Conclusions:

    • DiscriLens offers a powerful visual analytics approach to address algorithmic discrimination.
    • The tool provides valuable guidance for understanding and mitigating unfairness in machine learning.
    • Visualizing discrimination is crucial for ensuring equitable automated decision-making.