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Artificial intelligence and illusions of understanding in scientific research

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Listed:
  • Lisa Messeri

    (Yale University)

  • M. J. Crockett

    (Princeton University
    Princeton University)

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.

Suggested Citation

  • Lisa Messeri & M. J. Crockett, 2024. "Artificial intelligence and illusions of understanding in scientific research," Nature, Nature, vol. 627(8002), pages 49-58, March.
    • Handle: RePEc:nat:nature:v:627:y:2024:i:8002:d:10.1038_s41586-024-07146-0
    DOI: 10.1038/s41586-024-07146-0
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    Cited by:

    1. Daniel Albert & Stephan Billinger, 2024. "Reproducing and Extending Experiments in Behavioral Strategy with Large Language Models," Papers 2410.06932, arXiv.org.
    2. Martin Obschonka & Moren Levesque, 2024. "A Market for Lemons? Strategic Directions for a Vigilant Application of Artificial Intelligence in Entrepreneurship Research," Papers 2409.08890, arXiv.org.
    3. Jian-Qiao Zhu & Haijiang Yan & Thomas L. Griffiths, 2024. "Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice," Papers 2405.19313, arXiv.org.

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