The Gold in Bias: Maturing the AI Design Process through Verification
Bias in AI systems is typically framed as a flaw to be minimized, yet it also serves as a critical indicator of underlying weaknesses in data, modeling assumptions, and system design. Existing approaches often treat bias as an isolated problem rather than as evidence that can strengthen verification and governance across the AI lifecycle. This paper aims to reconceptualize bias as a diagnostic…
We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.
