Making Trouble: Techniques for Queering Data and AI Systems
- Anh-Ton Tran,
- Annabel Rothschild,
- Kay Kender,
- Ekat Osipova,
- Brian Kinnee,
- Jordan Taylor
- Georgia Institute of Technology,
- Vienna University of Technology,
- University of Washington,
- Carnegie Mellon University,
- University of Michigan,
- University of Sussex
Research Output:
Journal Article or Conference Article in Journal
Conference article
Peer-reviewOpen access
Publication Information
Output type
Research Output:
Journal Article or Conference Article in Journal
Conference article
Peer-reviewOriginal language
EnglishPages from-to (Number of pages)
Pages 381-384 (4 pages)Journal (Volume, Issue Number)
Proceedings of the Designing Interactive Systems ConferencePublication milestones
- Published - 01/07/2024
Publication status
Published - 01/07/2024
Publication IDs
- Scopus: 85198903258
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.
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Related Event
Title
ACM Conference on Designing Interactive Systems
Event type
ConferenceDegree of recognition
Local eventDate
01/07/2024 - 05/07/2024Location
IT University of CopenhagenCopenhagenDenmark
