Bound by the Bounty: Collaboratively Shaping Evaluation Processes for Queer AI Harms
This addresses the problem of AI harms for queer communities by proposing more inclusive auditing processes, though it is incremental as it builds on existing critiques of bias evaluation.
The paper tackled the problem of bias evaluation processes in AI failing to integrate marginalized community knowledge, by asking queer communities for their positions on auditing processes like bias bounties through a participatory workshop. The result was that participants questioned the ownership, incentives, and efficacy of bounties, advocating for community ownership and complementary participatory methods.
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).