当 AI 更擅长做科学时，人类为何以及如何继续做科学：基于人类理解与方向的框架
Why and How to Do Science When AI Is Better at It: A Framework Grounded in Human Understanding and Direction
AI 导读
一篇 arXiv 论文提出，科学的核心目标是人类对世界持久的集体理解，并据此给出判断研究任务能否交给 AI 的两部分测试：人类理解要求科学界仍能解释、辩护和质疑结论，人类方向要求重要问题的选择与发现的意义由可问责的人掌握。论文结合 CRediT 各研究阶段给出建议，并称该框架在 AI 超越人类研究者时依然成立。
正文 · 原文
Abstract:As AI begins to match or exceed human researchers at core scientific tasks, two questions become pressing: \emph{why} should humans keep doing science, and \emph{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 \emph{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, \emph{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, \emph{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.
| Subjects: | Artificial Intelligence (cs.AI); Computers and Society (cs.CY) |
| Cite as: | arXiv:2610.36116 [cs.AI] |
| (or arXiv:2610.36116v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.36116 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Hoda Heidari [view email]
[v1]
Mon, 26 Oct 2026 18:26:38 UTC (23 KB)
来源：arXiv cs.AI · arxiv.org