Why and How to Do Science When AI Is Better at It: A Framework Grounded in Human Understanding and Direction
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arXiv:2610.36116v1 Announce Type: new Abstract: As AI begins to match or exceed human researchers at core scientific tasks, two questions become pressing: why should humans keep doing science, and how should they use AI when they do? This paper argues that the second can be answered only by answering the first. It holds that one indispensable aim of science is durable, collective human understanding of the world, pursued in service of human ends and values. From this principle it derives a two-part test for when a research task can be delegated to AI. The first part, human understanding, requires that the scientific community remain able to explain, defend and contest the resulting claims, and retain the skills to do so. The second, human direction, requires that judgments about which questions matter and what findings mean stay with accountable humans. Applying the test to each phase of research in the Contributor Roles Taxonomy (CRediT) yields recommendations that fields can tailor to their questions and methods. The framework differs from existing guidance in three ways. It rests on an account of what science is for rather than on integrity norms. It holds even if AI outperforms human researchers, rather than depending on AI’s current limitations. And it judges each use of AI by its effects on human understanding and direction rather than by the type of tool, across every phase of research, including peer review and the training of new researchers. It closes with the reforms needed to keep science aimed at human understanding and direction.
Why It Matters
As AI begins to match or exceed human researchers at core scientific tasks, this paper asks why humans should keep doing science at all and how they should use AI when they do. It argues that one indispensable aim of science is durable, collective human understanding pursued in service of human ends, and derives a two-part test—human understanding and human direction—for when a research task can safely be delegated to AI. This matters because it gives fields a principled framework for deciding which parts of the scientific process to automate, connecting directly to the encyclopedia’s coverage of AI in science and technical frontiers, frontier AI labs, AI safety and governance, and the philosophy of knowledge and reasoning.
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