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Artificial intelligence and illusions of understanding in scientific research

Nature volume 627, pages 49–58 (2024)Cite this article

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Abstract

Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists’ visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community’s ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.

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Fig. 1: Illusions of understanding in AI-driven scientific research.

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Acknowledgements

We thank D. S. Bassett, W. J. Brady, S. Helmreich, S. Kapoor, T. Lombrozo, A. Narayanan, M. Salganik and A. J. te Velthuis for comments. We also thank C. Buckner and P. Winter for their feedback and suggestions.

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  1. These authors contributed equally: Lisa Messeri, M. J. Crockett

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  1. Department of Anthropology, Yale University, New Haven, CT, USA

    Lisa Messeri

  2. Department of Psychology, Princeton University, Princeton, NJ, USA

    M. J. Crockett

  3. University Center for Human Values, Princeton University, Princeton, NJ, USA

    M. J. Crockett

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Messeri, L., Crockett, M.J. Artificial intelligence and illusions of understanding in scientific research. Nature 627, 49–58 (2024). https://doi.org/10.1038/s41586-024-07146-0

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