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

Understand

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.