Explain the Black Box for the Sake of Science: the Scientific Method in the Era of Generative Artificial Intelligence

TL;DR

This paper discusses how explainable AI can support scientific discovery by making AI decision principles transparent, fostering collaboration between AI systems and human scientists to generate hypotheses and advance scientific knowledge.

Contribution

It introduces the concept of Explainable AI for Science, emphasizing the importance of interpretability in AI systems to aid scientific discovery before artificial general intelligence is achieved.

Findings

Explainable AI can facilitate scientific hypothesis formulation.

Interpretability-guided explanations can lead to new scientific insights.

Human reasoning remains vital in scientific discovery despite AI advancements.

Abstract

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.