Why an overreliance on AI-driven modelling is bad for science

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Without clear protocols to catch errors, artificial intelligence’s growing role in science could do more harm than good.

By

  1. Arvind Narayanan

    1. Arvind Narayanan is a computer scientist at Princeton University, Princeton, New Jersey, USA.

  2. Sayash Kapoor

    1. Sayash Kapoor is a computer scientist at the Center for Information Technology Policy, Princeton University, Princeton, New Jersey, USA.

A stylised, futuristic illustration of a blue hand holding a glowing lab flask containing a DNA strand and a pink sphere and surrounded by an atom. It is set against a cosmic, science-themed background with geometric diagrams.

Illustration: Denis Freitas

The use of artificial intelligence (AI) is exploding across many branches of science. Between 2012 and 2022, the average proportion of scientific papers engaging with AI, across 20 fields, quadrupled (see ‘AI’s rise in research’), including economics, geology, political science and psychology1.

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Nature 640, 312-314 (2025)

doi: https://doi.org/10.1038/d41586-025-01067-2

References

  1. Duede, E. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2405.15828 (2024).

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Competing Interests

The authors declare no competing financial interests.

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