The scientific method is the cornerstone of human progress across all branches of the natural and applied sciences, from understanding the human body to explaining how the universe works. The scientific method is based on identifying systematic rules or principles that describe the phenomenon of interest in a reproducible way that can be validated through experimental evidence. In the era of artificial intelligence (AI), there are discussions on how AI systems may discover new knowledge. We argue that, before the advent of artificial general intelligence, human complex reasoning for scientific discovery remains of vital importance. Yet, AI can be leveraged for scientific discovery via explainable AI. More specifically, knowing what data AI systems used to make decisions can be a point of contact with domain experts and scientists, that can lead to divergent or convergent views on a given scientific problem. Divergent views may spark further scientific investigations leading to new scientific knowledge. Convergent views may instead reassure that the AI system is operating within bounds deemed reasonable to humans. The latter point addresses the trustworthiness requirement that is indispensable for critical applications in the applied sciences, such as medicine.

翻译：科学方法是人类在自然科学与应用科学各分支领域取得进步的基石，从理解人体结构到阐释宇宙运行规律皆基于此。科学方法的核心在于识别能够以可复现方式描述目标现象的系统性规则或原理，并通过实验证据加以验证。在人工智能时代，关于AI系统如何发现新知识的讨论日益增多。我们认为，在通用人工智能到来之前，人类用于科学发现的复杂推理能力仍具有至关重要的意义。然而，通过可解释人工智能技术，AI能够有效助力科学发现。具体而言，了解AI系统决策所依据的数据可以成为与领域专家和科学家建立联系的切入点，这可能导致对特定科学问题产生分歧性或趋同性观点。分歧性观点可能引发更深入的科学研究，从而催生新的科学知识；而趋同性观点则能增强人们对AI系统在人类认可合理范围内运行的信心。后一点对于应用科学（如医学）关键领域中不可或缺的可信度要求具有重要意义。