An editorial by Charles Branas and Bruce Levine proposes norms for the use of AI in science. The authors distinguish between using AI tools for non-generative tasks, such as data classification or copyediting, and using AI tools to generate scientific ideas or seed research questions. While the former is broadly acceptable, they argue, the latter deserves more scrutiny and should be disclosed in scientific manuscripts. The use of generative AI for the core intellectual, creative, ethical, interpretive, or accountability-bearing work of research, where AI replaces human interpretation and judgement, should be disallowed, according to the authors. Additionally, the authors propose that researchers should not use AI to generate their own initial research ideas, research questions, or to make ethical determinations, risk assessments, or policy recommendations.
AI can reduce science’s happy accidents and human insights if everyone is relying on the same tools with similar inputs, the authors argue. They note that science should not be unthinkingly optimized, as if it were a manufacturing workflow producing standardized outputs. Science requires a wide diversity of inputs, approaches, and ideas. Generative AI is trained to produce probable outputs based on known patterns and trained on existing data, whereas scientific breakthroughs often require departing from prior assumptions and breaking free of typical patterns. Serendipity counts in science.
According to the authors, the originating research question and final evidentiary judgment in every scientific publication should be produced by human minds.
PNAS Nexus
AI and authorship: Norms and uses to preserve human-led science
8-Sep-2026