Mithralabel: Flexible dataset nutritional labels for responsible data science

Chenkai Sun, Abolfazl Asudeh, H. V. Jagadish, Bill Howe, Julia Stoyanovich

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

    Abstract

    Using inappropriate datasets for data science tasks can be harmful, especially for applications that impact humans. Targeting data ethics, we demonstrate MithraLabel, a system for generating task-specific information about a dataset, in the form of a set of visual widgets, as a flexible "nutritional label" that provides a user with information to determine the fitness of the dataset for the task at hand.

    Original languageEnglish (US)
    Title of host publicationCIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management
    PublisherAssociation for Computing Machinery
    Pages2893-2896
    Number of pages4
    ISBN (Electronic)9781450369763
    DOIs
    StatePublished - Nov 3 2019
    Event28th ACM International Conference on Information and Knowledge Management, CIKM 2019 - Beijing, China
    Duration: Nov 3 2019Nov 7 2019

    Publication series

    NameInternational Conference on Information and Knowledge Management, Proceedings

    Conference

    Conference28th ACM International Conference on Information and Knowledge Management, CIKM 2019
    CountryChina
    CityBeijing
    Period11/3/1911/7/19

    Keywords

    • Accountability
    • Data Ethics
    • Fairness
    • Machine Bias
    • Transparency

    ASJC Scopus subject areas

    • Business, Management and Accounting(all)
    • Decision Sciences(all)

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  • Cite this

    Sun, C., Asudeh, A., Jagadish, H. V., Howe, B., & Stoyanovich, J. (2019). Mithralabel: Flexible dataset nutritional labels for responsible data science. In CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 2893-2896). (International Conference on Information and Knowledge Management, Proceedings). Association for Computing Machinery. https://doi.org/10.1145/3357384.3357853