Bound by the Bounty: Collaboratively Shaping Evaluation Processes for Queer AI Harms

TL;DR

This paper explores how participatory, community-driven approaches can improve AI bias evaluation, emphasizing queer communities' perspectives and advocating for shared ownership and co-creation in bias auditing processes.

Contribution

It introduces a participatory workshop with queer communities to critique and redesign bias bounties, highlighting the need for community ownership and broader engagement.

Findings

Participants questioned ownership and incentives of bias bounties.

Community feedback extends beyond traditional bounty scope.

Advocates for co-creation and participatory approaches in bias evaluation.

Abstract

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).