Record link: 
http://hdl.handle.net/20.500.12708/201675
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Title: 
Making Trouble: Techniques for Queering Data and AI Systems
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Citation: 
Tran, A.-T., Rothschild, A., Kender, K., Osipova, E., Kinnee, B., Taylor, J., Meyer, L. S., Haimson, O. L., Light, A., & Disalvo, C. (2024). Making Trouble: Techniques for Queering Data and AI Systems. In DIS ’24 Companion: Companion Publication of the 2024 ACM Designing Interactive Systems Conference (pp. 381–384). ACM. https://doi.org/10.1145/3656156.3658393
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Publisher DOI: 
10.1145/3656156.3658393
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Publication Type: 
Inproceedings - Abstract Book Contribution
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Language: 
English
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Authors: 
Tran, Anh-Ton 
Rothschild, Annabel 
Kender, Kay 
Osipova, Ekat 
Kinnee, Brian 
Taylor, Jordan 
Meyer, Louie Søs 
Haimson, Oliver L. 
Light, Ann 
Disalvo, Carl 
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Organisational Unit: 
E193-05 - Forschungsbereich Human Computer Interaction 
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Published in: 
DIS '24 Companion: Companion Publication of the 2024 ACM Designing Interactive Systems Conference 
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ISBN: 
9798400706325
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Date (published): 
2024
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Event name: 
DIS '24 Companion: Companion Publication of the 2024 ACM Designing Interactive Systems Conference 
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Event date: 
1-Jul-2024 - 5-Jul-2024
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Event place: 
Copenhagen, Denmark
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Number of Pages: 
4
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Publisher: 
ACM
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Peer reviewed: 
Yes
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Keywords: 
AI Systems; Algorithmic Systems; Data; Design; Human-in-the-Loop (HITL); Machine Learning; Queering; Queering-the-Loop
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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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Project title: 
Zugang mit Interaktiven Technologien Erfahren: 101117519 (European Commission) 
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Research Areas: 
Mathematical and Algorithmic Foundations: 30%
Computer Science Foundations: 40%
Computational System Design: 30%
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Science Branch: 
1020 - Informatik: 90%
1010 - Mathematik: 10%
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Appears in Collections:
Conference Paper

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