ViCE

Oscar Gomez, Steffen Holter, Jun Yuan, Enrico Bertini

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

    Abstract

    The continued improvements in the predictive accuracy of machine learning models have allowed for their widespread practical application. Yet, many decisions made with seemingly accurate models still require verification by domain experts. In addition, end-users of a model also want to understand the reasons behind specific decisions. Thus, the need for interpretability is increasingly paramount. In this paper we present an interactive visual analytics tool, ViCE, that generates counterfactual explanations to contextualize and evaluate model decisions. Each sample is assessed to identify the minimal set of changes needed to flip the model's output. These explanations aim to provide end-users with personalized actionable insights with which to understand, and possibly contest or improve, automated decisions. The results are effectively displayed in a visual interface where counterfactual explanations are highlighted and interactive methods are provided for users to explore the data and model. The functionality of the tool is demonstrated by its application to a home equity line of credit dataset.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 25th International Conference on Intelligent User Interfaces, IUI 2020
    PublisherAssociation for Computing Machinery
    Pages531-535
    Number of pages5
    ISBN (Electronic)9781450371186
    DOIs
    StatePublished - Mar 17 2020
    Event25th ACM International Conference on Intelligent User Interfaces, IUI 2020 - Cagliari, Italy
    Duration: Mar 17 2020Mar 20 2020

    Publication series

    NameInternational Conference on Intelligent User Interfaces, Proceedings IUI

    Conference

    Conference25th ACM International Conference on Intelligent User Interfaces, IUI 2020
    CountryItaly
    CityCagliari
    Period3/17/203/20/20

    Keywords

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

    ASJC Scopus subject areas

    • Software
    • Human-Computer Interaction

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  • Cite this

    Gomez, O., Holter, S., Yuan, J., & Bertini, E. (2020). ViCE. In Proceedings of the 25th International Conference on Intelligent User Interfaces, IUI 2020 (pp. 531-535). (International Conference on Intelligent User Interfaces, Proceedings IUI). Association for Computing Machinery. https://doi.org/10.1145/3377325.3377536