AdViCE: Aggregated Visual Counterfactual Explanations for Machine Learning Model Validation

Oscar Gomez, Steffen Holter, Jun Yuan, Enrico Bertini

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Rapid improvements in the performance of machine learning models have pushed them to the forefront of data-driven decision-making. Meanwhile, the increased integration of these models into various application domains has further highlighted the need for greater interpretability and transparency. To identify problems such as bias, overfitting, and incorrect correlations, data scientists require tools that explain the mechanisms with which these model decisions are made. In this paper we introduce AdViCE, a visual analytics tool that aims to guide users in black-box model debugging and validation. The solution rests on two main visual user interface innovations: (1) an interactive visualization design that enables the comparison of decisions on user-defined data subsets; (2) an algorithm and visual design to compute and visualize counterfactual explanations - explanations that depict model outcomes when data features are perturbed from their original values. We provide a demonstration of the tool through a use case that showcases the capabilities and potential limitations of the proposed approach.

    Original languageEnglish (US)
    Title of host publicationProceedings - 2021 IEEE Visualization Conference - Short Papers, VIS 2021
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages31-35
    Number of pages5
    ISBN (Electronic)9781665433358
    DOIs
    StatePublished - 2021
    Event2021 IEEE Visualization Conference, VIS 2021 - Virtual, Online, United States
    Duration: Oct 24 2021Oct 29 2021

    Publication series

    NameProceedings - 2021 IEEE Visualization Conference - Short Papers, VIS 2021

    Conference

    Conference2021 IEEE Visualization Conference, VIS 2021
    Country/TerritoryUnited States
    CityVirtual, Online
    Period10/24/2110/29/21

    Keywords

    • counterfactual explanations
    • data visualization
    • explainability
    • interpretability
    • Machine learning

    ASJC Scopus subject areas

    • Computer Science Applications
    • Media Technology
    • Modeling and Simulation

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