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Quantifying the use and potential benefits of artificial intelligence in scientific research

Nature Human Behaviour volume 8, pages 2281–2292 (2024)Cite this article

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Abstract

The rapid advancement of artificial intelligence (AI) is poised to reshape almost every line of work. Despite enormous efforts devoted to understanding AI’s economic impacts, we lack a systematic understanding of the benefits to scientific research associated with the use of AI. Here we develop a measurement framework to estimate the direct use of AI and associated benefits in science. We find that the use and benefits of AI appear widespread throughout the sciences, growing especially rapidly since 2015. However, there is a substantial gap between AI education and its application in research, highlighting a misalignment between AI expertise supply and demand. Our analysis also reveals demographic disparities, with disciplines with higher proportions of women or Black scientists reaping fewer benefits from AI, potentially exacerbating existing inequalities in science. These findings have implications for the equity and sustainability of the research enterprise, especially as the integration of AI with science continues to deepen.

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Fig. 1: Measuring the direct use of AI in scientific research.
Fig. 2: Measuring the potential benefits of AI and discipline heterogeneity.
Fig. 3: Misalignment between AI education and AI use and benefits, but growing knowledge demand for AI.
Fig. 4: Gender and racial disparities in the use and benefits of AI across disciplines.

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Data availability

The MAG data are available at https://doi.org/10.5281/zenodo.6511057 (ref. 131) and ref. 132. The USPTO patent data are available at https://patentsview.org. The OSP dataset is available from the paper at https://doi.org/10.1073/pnas.1804247115. The SDR data are available at https://www.nsf.gov/statistics/srvydoctoratework, and the datasets used in this study are de-identified, containing only summary statistics for each discipline. The data met the assumption of tests in the analysis. The data necessary to reproduce all main plots in this paper are available at https://kellogg-cssi.github.io/ai4science.

Code availability

Data are linked and analysed with customized code in Python 3 using standard software packages within these programmes, including pandas 1.3.5, numpy 1.21.5, scipy 1.7.3, matplotlib 3.5.1, seaborn 0.11.2, spacy 3.7.2, nomquamgender 0.1.0, demographicx 0.0.1 and others. The code necessary to reproduce all main plots and statistical analyses is available at https://kellogg-cssi.github.io/ai4science.

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Acknowledgements

We thank Y. Yin, Y. Qian, B. Wang, N. Dehmamy, L. Varshney, L. Miao, L. Wu, A. Freilich and all members of the Center for Science of Science and Innovation (CSSI) at Northwestern University for helpful discussions. This work is supported by the Air Force Office of Scientific Research FA9550-19-1-0354 (D.W.), the National Science Foundation SBE 1829344, TIP 1123649-464363//2241237, and TIP 2404035 (D.W.), the Alfred P. Sloan Foundation G-2019-12485 (D.W.), and the Peter G. Peterson Foundation 21048 (D.W.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper.

Author information

Authors and Affiliations

  1. Center for Science of Science and Innovation, Northwestern University, Evanston, IL, USA

    Jian Gao & Dashun Wang

  2. Kellogg School of Management, Northwestern University, Evanston, IL, USA

    Jian Gao & Dashun Wang

  3. Ryan Institute on Complexity, Northwestern University, Evanston, IL, USA

    Jian Gao & Dashun Wang

  4. Faculty of Social Sciences, The University of Hong Kong, Hong Kong SAR, China

    Jian Gao

  5. McCormick School of Engineering, Northwestern University, Evanston, IL, USA

    Dashun Wang

Authors
  1. Jian Gao
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  2. Dashun Wang
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Contributions

J.G. and D.W. conceived the idea. D.W. supervised the project. J.G. collected data and performed analyses. J.G. and D.W. analysed the results, interpreted the findings and wrote the paper.

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Correspondence to Dashun Wang.

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Supplementary Notes 1–9, Figs. 1–17, Tables 1 and 2, and references.

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Gao, J., Wang, D. Quantifying the use and potential benefits of artificial intelligence in scientific research. Nat Hum Behav 8, 2281–2292 (2024). https://doi.org/10.1038/s41562-024-02020-5

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