2 research outputs found
Bound by the Bounty: Collaboratively Shaping Evaluation Processes for Queer AI Harms
Bias evaluation benchmarks and dataset and model documentation have emerged
as central processes for assessing the biases and harms of artificial
intelligence (AI) systems. However, these auditing processes have been
criticized for their failure to integrate the knowledge of marginalized
communities and consider the power dynamics between auditors and the
communities. Consequently, modes of bias evaluation have been proposed that
engage impacted communities in identifying and assessing the harms of AI
systems (e.g., bias bounties). Even so, asking what marginalized communities
want from such auditing processes has been neglected. In this paper, we ask
queer communities for their positions on, and desires from, auditing processes.
To this end, we organized a participatory workshop to critique and redesign
bias bounties from queer perspectives. We found that when given space, the
scope of feedback from workshop participants goes far beyond what bias bounties
afford, with participants questioning the ownership, incentives, and efficacy
of bounties. We conclude by advocating for community ownership of bounties and
complementing bounties with participatory processes (e.g., co-creation).Comment: To appear at AIES 202
Queer In AI: A Case Study in Community-Led Participatory AI
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