{"id":79442,"date":"2026-01-21T03:23:24","date_gmt":"2026-01-21T08:23:24","guid":{"rendered":"https:\/\/world.edu\/?p=79442"},"modified":"2026-01-21T03:23:24","modified_gmt":"2026-01-21T08:23:24","slug":"ai-cannot-automate-science-a-philosopher-explains-the-uniquely-human-aspects-of-doing-research","status":"publish","type":"post","link":"https:\/\/world.edu\/ai-cannot-automate-science-a-philosopher-explains-the-uniquely-human-aspects-of-doing-research\/","title":{"rendered":"AI cannot automate science \u2013 a philosopher explains the uniquely human aspects of doing research"},"content":{"rendered":"

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Consistent with the general trend of\u00a0incorporating artificial intelligence into nearly every field<\/a>, researchers and politicians are increasingly using AI models trained on scientific data to infer answers to scientific questions. But can AI ultimately replace scientists?<\/p>\n

The Trump administration signed an executive order on Nov. 24, 2025, that announced\u00a0the Genesis Mission<\/a>, an initiative to build and train a series of\u00a0AI agents<\/a>\u00a0on federal scientific datasets \u201cto test new hypotheses, automate research workflows, and accelerate scientific breakthroughs.\u201d<\/p>\n

So far, the accomplishments of these so-called\u00a0AI scientists have been mixed<\/a>. On the one hand, AI systems can process vast datasets and detect subtle correlations that humans are unable to detect. On the other hand, their lack of commonsense reasoning can result in unrealistic or irrelevant experimental recommendations.<\/p>\n

While AI can assist in tasks that are part of the scientific process, it is still far away from automating science \u2013 and may never be able to.\u00a0As a philosopher<\/a>\u00a0who studies both the history and the conceptual foundations of science, I see several problems with the idea that AI systems can \u201cdo science\u201d without or even better than humans.<\/p>\n

AI models can only learn from human scientists<\/strong><\/h4>\n

AI models do not learn directly from the real world: They have to be\u00a0\u201ctold\u201d what the world is like<\/a>\u00a0by their human designers. Without human scientists overseeing the construction of the digital \u201cworld\u201d in which the model operates \u2013 that is, the datasets used for training and testing its algorithms \u2013 the breakthroughs that AI facilitates wouldn\u2019t be possible.<\/p>\n

Consider the AI model AlphaFold. Its developers were awarded the\u00a02024 Nobel Prize in chemistry<\/a>\u00a0for the model\u2019s ability to infer the structure of proteins in human cells. Because so many biological functions depend on proteins, the ability to quickly generate protein structures to test via simulations has the potential to accelerate drug design, trace how diseases develop and advance other areas of biomedical research.<\/p>\n

As practical as it may be, however, an AI system like AlphaFold does not provide new knowledge about proteins, diseases or more effective drugs on its own. It simply makes it possible to analyze existing information more efficiently.
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