Why an overreliance on AI-driven modelling is bad for science
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
These biases may result from partialities in AI training data, algorithms, models, the data inputted into the AI for analysis or processing, or some combination of these.
“These biases may result from partialities in AI training data, algorithms, models, the data inputted into the AI for analysis or processing, or some combination of these [23, 57, 58].”
From Autonomous artificial intelligence, scientific research, and human values (2026) · cites this paper as [58]
Cites
Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
Breiman, L. (2001) · Statistical Science · doi:10.1214/ss/1009213726 · Cited by 4,340 · see citing works
Leakage and the reproducibility crisis in machine-learning-based science
Kapoor, S., & Narayanan, A. (2023) · Patterns · doi:10.1016/j.patter.2023.100804 · Cited by 1,387 · see citing works
Self-citationRoberts, M., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Aviles-Rivero, A. I., Etmann, C., McCague, C., Beer, L., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00307-0 · Cited by 1,044 · see citing works
Roberts, M. T., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Avilés-Rivero, A. I., Etmann, C., McCague, C., Beer, L., et al. (2020) · Apollo (University of Cambridge) · doi:10.17863/cam.64000 · Cited by 902 · see citing works
Artificial intelligence and illusions of understanding in scientific research
Messeri, L., & Crockett, M. J. (2024) · Nature · doi:10.1038/s41586-024-07146-0 · Cited by 707 · see citing works
REFORMS: Consensus-based Recommendations for Machine-learning-based Science
Kapoor, S., Cantrell, E. M., Peng, K., Pham, T. H., Bail, C. A., Gundersen, O. E., Hofman, J. M., Hullman, J. R., Lones, M. A., Malik, M. M., et al. (2024) · Science Advances · doi:10.1126/sciadv.adk3452 · Cited by 192 · see citing works
Self-citationOil & Water? Diffusion of AI Within and Across Scientific FieldsPreprint
Duede, E., Dolan, W., Bauer, A., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2405.15828 · Cited by 11 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Autonomous artificial intelligence, scientific research, and human values
Resnik, D. B., Hosseini, M., & Hauswald, R. (2026) · AI and Ethics · doi:10.1007/s43681-025-00908-0 · Cited by 15 · see citing works
- Authors
- Narayanan, A., & Kapoor, S.
- Affiliations
- Princeton University (US)
- Corresponding author
- Narayanan, Arvind
- Venue
- Nature
- Published
- 2025-04-07
- Volume
- 640
- Issue
- 8058
- Pages
- 312-314
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W4409235233
- Cited by
- 21
- References
- 7
- Citations by year
- 2026: 10 · 2025: 11
- FWCI
- 21.96
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Keywords
- artificial intelligence in science, error detection, reproducibility, scientific integrity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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