Organizers Of QueerInAI, .;
Ovalle, A;
Subramonian, A;
Singh, A;
Voelcker, C;
Sutherland, DJ;
Locatelli, D;
... Stark, L; + view all
(2023)
Queer In AI: A Case Study in Community-Led Participatory AI.
In:
FAccT '23: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency.
(pp. pp. 1882-1895).
Association for Computing Machinery: New York, United States.
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Abstract
Queerness and queer people face an uncertain future in the face of ever more widely deployed and invasive artificial intelligence (AI). These technologies have caused numerous harms to queer people, including privacy violations, censoring and downranking queer content, exposing queer people and spaces to harassment by making them hypervisible, deadnaming and outing queer people. More broadly, they have violated core tenets of queerness by classifying and controlling queer identities. In response to this, the queer community in AI has organized Queer in AI, a global, decentralized, volunteer-run grassroots organization that employs intersectional and community-led participatory design to build an inclusive and equitable AI future. In this paper, we present Queer in AI as a case study for community-led participatory design in AI. We examine how participatory design and intersectional tenets started and shaped this community’s programs over the years. We discuss different challenges that emerged in the process, look at ways this organization has fallen short of operationalizing participatory and intersectional principles, and then assess the organization’s impact. Queer in AI provides important lessons and insights for practitioners and theorists of participatory methods broadly through its rejection of hierarchy in favor of decentralization, success at building aid and programs by and for the queer community, and effort to change actors and institutions outside of the queer community. Finally, we theorize how communities like Queer in AI contribute to the participatory design in AI more broadly by fostering cultures of participation in AI, welcoming and empowering marginalized participants, critiquing poor or exploitative participatory practices, and bringing participation to institutions outside of individual research projects. Queer in AI’s work serves as a case study of grassroots activism and participatory methods within AI, demonstrating the potential of community-led participatory methods and intersectional praxis, while also providing challenges, case studies, and nuanced insights to researchers developing and using participatory methods.
| Type: | Proceedings paper |
|---|---|
| Title: | Queer In AI: A Case Study in Community-Led Participatory AI |
| Event: | FAccT '23: the 2023 ACM Conference on Fairness, Accountability, and Transparency |
| ISBN-13: | 9798400701924 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3593013.3594134 |
| Publisher version: | https://doi.org/10.1145/3593013.3594134 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10173681 |
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