Making Trouble: Techniques for Queering Data and AI Systems

Anh-Ton Tran, Annabel Rothschild, Kay Kender, Ekat Osipova, Brian Kinnee, Jordan Taylor, Louie Søs Meyer, Oliver L. Haimson, Ann Light, Carl DiSalvo

    Research output: Journal Article or Conference Article in JournalConference articleResearchpeer-review

    Abstract

    This one day workshop will explore queering as a design technique for troubling data and AI systems, ranging from quotidian personal data to recent Generative AI tools. By surfacing numerous instances of queering data or AI, we will come together to develop an archive of techniques for queering or artful subversion. From this archive, participants will select a technique and develop a speculative prototype or artifact via critical making. In doing so, we resist techno-determinism and conventional narratives of AI harms and benefits by tracing queer possibilities outside these categories.
    Original languageEnglish
    JournalProceedings of the 2024 ACM Designing Interactive Systems Conference
    Pages (from-to) 381-384
    Number of pages4
    DOIs
    Publication statusPublished - 1 Jul 2024

    Keywords

    • Queering
    • Data
    • Human-in-the-Loop (HITL)
    • AI Systems, Design
    • Machine Learning
    • Algorithmic Systems
    • Queering-the-Loop
    • Design

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