Explain the Black Box for the Sake of Science: the Scientific Method in the Era of Generative Artificial Intelligence
It addresses the challenge of integrating AI into scientific discovery for researchers and scientists, but is incremental as it builds on existing explainable AI concepts.
The paper argues that human reasoning remains crucial for scientific discovery in the era of generative AI, proposing that explainable AI can bridge AI systems and domain experts to generate new scientific knowledge through interpretability-guided explanations.
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 generative artificial intelligence, there are discussions on how AI systems may discover new knowledge. We argue that human complex reasoning for scientific discovery remains of vital importance, at least before the advent of artificial general intelligence. Yet, AI can be leveraged for scientific discovery via explainable AI. More specifically, knowing the `principles' the 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 interpretability-guided explanations (IGEs), and possibly to new scientific knowledge. We define this field as Explainable AI for Science, where domain experts -- potentially assisted by generative AI -- formulate scientific hypotheses and explanations based on the interpretability of a predictive AI system.