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Transparency and reproducibility in artificial intelligence
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Open Access articles citing this article. Deep learning in the stock market—a systematic survey of practice, backtesting, and applications Kenniy Olorunnimbe & Herna Viktor Artificial Intelligence Review Open Access 30 June 2022 Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening Jenny Yang , Andrew A. S. Soltan & David A. Clifton npj Digital Medicine Open Access 07 June 2022 Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases Chirag Gupta , Pramod Chandrashekar … Daifeng Wang Journal of Neurodevelopmental Disorders Open Access 02 May 2022
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“In addition, AI's transparency and interpretability are challenged by factors such as patient privacy, algorithm interpretability, publication bias, etc. 39,40.”
From Artificial intelligence in pancreatic cancer (2022) · cites this paper as [40]
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“highlighting the risk of using corporate models in a sensitive area such as medicine, where patient privacy, transparency and reliability are essential74,76”
From Evaluation and mitigation of the limitations of large language models in clinical decision-making (2024) · cites this paper as [76]
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“When generative AI follows the release-by-blogpost model, it is reaping the benefits of mimicking scientific communication -including associations of reproducibility and rigour [21, 23]- without actually doing the work.”
From Rethinking open source generative AI: open washing and the EU AI Act (2024) · cites this paper as [23]
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“One of the most effective methods of mitigating AI biases is to ensure the use of diverse and representative datasets during AI development and training [67, 87].”
From Fairness of artificial intelligence in healthcare: review and recommendations (2024) · cites this paper as [87]
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“A critical component of AI evaluation and development more generally is ensuring transparency and reproducibility, allowing independent research groups to review and build on prior scientific progress (196).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [196]
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“In addition, the lack of transparency and reproducibility of AI computational algorithm methods and code in many published studies threatens scientific progress and discovery, which require that independent scientists are able to scrutinize and reproduce results98.”
From Smart wearable devices in cardiovascular care: Where we are and how to move forward (2021) · cites this paper as [98]
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“Because of the particular significance of addressing the data-related concerns, different countries have developed policies and laws [160].”
From Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2022) · cites this paper as [160]
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“Unfortunately, though, the majority of existing AI/ML models remain in specialized journals, with poorly maintained code and variable rigor in validation.283”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [283]
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Cites
A survey on Image Data Augmentation for Deep Learning
Shorten, C., & Khoshgoftaar, T. M. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0197-0 · Cited by 13,203
International evaluation of an AI system for breast cancer screening
McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1799-6 · Cited by 3,287
Text Data Augmentation for Deep Learning
Shorten, C., Khoshgoftaar, T. M., & Furht, B. (2021) · Journal Of Big Data · doi:10.1186/s40537-021-00492-0 · Cited by 1,852
(2015) · neural information processing systems · Cited by 912
A curated mammography data set for use in computer-aided detection and diagnosis research
Lee, R. S., Gimenez, F., Hoogi, A., Miyake, K. K., Gorovoy, M., & Rubin, D. L. (2017) · Scientific Data · doi:10.1038/sdata.2017.177 · Cited by 859
Artificial intelligence faces reproducibility crisis
Hutson, M. (2018) · Science · doi:10.1126/science.359.6377.725 · Cited by 573
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Stodden, V., Mcnutt, M. K., Bailey, D. H., Deelman, E., Gil, Y., Hanson, B., Heroux, M. A., Ioannidis, J. P. A., & Taufer, M. (2016) · Science · doi:10.1126/science.aah6168 · Cited by 464
Self-citationBluemke, D. A., Moy, L., Bredella, M. A., Ertl-Wagner, B. B., Fowler, K. J., Goh, V. J., Halpern, E. F., Hess, C. P., Schiebler, M. L., & Weiss, C. R. (2020) · Radiology · doi:10.1148/radiol.2019192515 · Cited by 343
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Iqbal, S. A., Wallach, J. D., Khoury, M. J., Schully, S. D., & Ioannidis, J. P. A. (2016) · PLOS Biology · doi:10.1371/journal.pbio.1002333 · Cited by 310
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Gundersen, O. E., & Kjensmo, S. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11503 · Cited by 293 · cited 1×
“Even with extensive description, reproducing complex computational pipelines based purely on text is a subjective and challenging task 9 .”
Background
Research works printed but not linked: 1
- Sculley, D. et al. in Advances in Neural Information Processing Systems 28 (eds Cortes, C. et al.) 2503–2511 (Curran Associates, Inc., 2015).
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BackgroundBirch, J. (2024) · Oxford University Press eBooks · doi:10.1093/9780191966729.003.0011 · Cited by 1
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“As Haibe-Kains et al. argue, “computational reproducibility is indispensable for high-quality AI applications”, and without code the only option is to “replicate methods from textual description,” which is often infeasible [71].”
Ray, S. (2025) · Preprints.org · doi:10.20944/preprints202507.0417.v1
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Hajal, A. A., & Meslamani, A. Z. A. (2026) · Computational Toxicology · doi:10.1016/j.comtox.2026.100429
Metadata
- Authors
- Haibe-Kains, B., Adam, G. A., Hosny, A., Khodakarami, F., Massive Analysis Quality Control (MAQC) Society Board of Directors, Shraddha, T., Kusko, R., Sansone, S.-A., Tong, W., Wolfinger, R. D., Mason, C. E., Jones, W., Dopazo, J., Furlanello, C., Waldron, L., Wang, B., McIntosh, C., Goldenberg, A., Kundaje, A., Greene, C. S., Broderick, T., Hoffman, M. M., Leek, J. T., Korthauer, K., Huber, W., Brazma, A., Pineau, J., Tibshirani, R., Hastie, T., Ioannidis, J. P. A., Quackenbush, J., & Aerts, H. J. W. L.
- Affiliations
- University of Toronto (CA)
- Brigham and Women's Hospital (US)
- University Health Network (CA)
- National Center for Toxicological Research (US)
- University of Oxford (GB)
- SAS Institute (United States) (US)
- Q2 Solutions (United States) (US)
- City University of New York (US)
- Canadian Institute for Advanced Research (CA)
- Alex's Lemonade Stand Foundation (US)
- University of British Columbia (CA)
- European Bioinformatics Institute (GB)
- Stanford Medicine (US)
- ORCID
- Haibe-Kains, Benjamin
- Adam, George Alexandru
- Hosny, Ahmed
- Khodakarami, Farnoosh
- Kusko, Rebecca
- Sansone, Susanna-Assunta
- Tong, Weida
- Mason, Christopher E.
- Jones, Wendell
- Furlanello, Cesare
- Waldron, Levi
- Wang, Bo
- McIntosh, Chris
- Goldenberg, Anna
- Kundaje, Anshul
- Greene, Casey S.
- Broderick, Tamara
- Hoffman, Michael M.
- Leek, Jeffrey T.
- Korthauer, Keegan
- Huber, Wolfgang
- Brazma, Alvis
- Pineau, Joelle
- Tibshirani, Robert
- Hastie, Trevor
- Ioannidis, John P. A.
- Quackenbush, John
- Aerts, Hugo J. W. L.
- Venue
- Nature
- Published
- 2020-10-14
- Volume
- 586
- Issue
- 7829
- Pages
- E14-E16
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC8144864
- OpenAlex
- W3008058947
- Cited by
- 540
- References
- 18
- Citations by year
- 2026: 83 · 2025: 91 · 2024: 97 · 2023: 110 · 2022: 79 · 2021: 70 · 2020: 10
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature (publishedVersion, other-oa)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion, other-oa)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- RISalud Institutional Health Repository of Andalucía (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Funders
- Biotechnology and Biological Sciences Research Council (BB/L005069/2, BB/E025080/1, BB/L005069/1, BB/J020265/1, BB/L005050/1)
- National Cancer Institute (U24CA180996, R01CA237170, CA237170)
- National Institute of General Medical Sciences (DP2GM123485)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Keywords
- reproducibility, explainability, deep learning, backtesting, generalizability, machine learning in healthcare, intellectual and developmental disabilities
- MeSH terms
- Algorithms, Artificial Intelligence, Reproducibility of Results
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