Making Transparency Influencers: A Case Study of an Educational Approach to Improve Responsible AI Practices in News and Media

Andrew Bell, Julia Stoyanovich

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

    Abstract

    Concerns about the risks posed by artificial intelligence (AI) have resulted in growing interest in algorithmic transparency. While algorithmic transparency is well-studied, there is evidence that many organizations do not value implementing transparency. In this case study, we test a ground-up approach to ensuring better real-world algorithmic transparency by creating transparency influencers - motivated individuals within organizations who advocate for transparency. We held an interactive online workshop on algorithmic transparency and advocacy for 15 professionals from news, media, and journalism. We reflect on workshop design choices and presents insights from participant interviews. We found positive evidence for our approach: In the days following the workshop, three participants had done pro-transparency advocacy. Notably, one of them advocated for algorithmic transparency at an organization-wide AI strategy meeting. In the words of a participant: "if you are questioning whether or not you need to tell people [about AI], you need to tell people."

    Original languageEnglish (US)
    Title of host publicationCHI 2024 - Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Sytems
    PublisherAssociation for Computing Machinery
    ISBN (Electronic)9798400703317
    DOIs
    StatePublished - May 11 2024
    Event2024 CHI Conference on Human Factors in Computing Sytems, CHI EA 2024 - Hybrid, Honolulu, United States
    Duration: May 11 2024May 16 2024

    Publication series

    NameConference on Human Factors in Computing Systems - Proceedings

    Conference

    Conference2024 CHI Conference on Human Factors in Computing Sytems, CHI EA 2024
    Country/TerritoryUnited States
    CityHybrid, Honolulu
    Period5/11/245/16/24

    Keywords

    • artificial intelligence
    • explainability
    • machine learning
    • tempered radicals
    • Transparency

    ASJC Scopus subject areas

    • Human-Computer Interaction
    • Computer Graphics and Computer-Aided Design
    • Software

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