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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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LIU, J., Dong, Y., Brooks, I. S., Cheung, W. C., Nguyen, V. L., & Jan, Y. (2026) · Sensors · doi:10.3390/s26175394 · Cited by 1
Enhancing reproducibility in hybrid Earth system models
Chen, M., Zhu, Z., Wagener, T., Boers, N., Müller, R. D., Strobl, J., Camps‐Valls, G., Batty, M., Jakeman, A. J., Kolditz, O., et al. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-026-01299-5 · Cited by 1
“Unlike process-based models grounded in physically interpretable mechanisms, AI modules often rely on opaque learning processes, undocumented heuristics and platform-dependent computation, making it challenging to trace decision pathways or to consistently reproduce previous results 12,62 .”
InfluentialArtificial intelligence in biomarker discovery for diseases: diagnostic and therapeutic prospects
Dinç, R., & Ardıç, N. (2026) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-026-02946-4 · Cited by 1
Eloyan, A., & Rose, S. (2021) · Observational Studies · doi:10.1353/obs.2021.0009 · Cited by 1
New approaches to understanding the impact of computers on work and education
Révai, N., Staneva, M., & Baret, A. (2021) · Educational research and innovation · doi:10.1787/65774a32-en · Cited by 1
Oncology Informatics, AI, and Drug Discovery
Santra, D. (2022) · Springer eBooks · doi:10.1007/978-981-16-4752-9_20 · Cited by 1
Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging
(2022) · Springer eBooks · doi:10.1007/978-3-031-00119-2 · Cited by 1
Characterization of AI Model Configurations for Model Reuse
Bajcsy, P., Majurski, M. P., Cleveland, T. E., Carrasco, M., & Keyrouz, W. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25069-9_30 · Cited by 1
Reproducibility Requires Consolidated Artifacts
Fostiropoulos, I., Brown, B., & Itti, L. (2023) · IEEE/ACM International Conference on AI Engineering – Software Engineering for AI (CAIN) · doi:10.1109/cain58948.2023.00025 · Cited by 1
“[1] identifies missing and convoluted artifacts as one of the main causes of non-reproducible research.”
BackgroundData Anonymization and Open Sharing Are Key to a Sustainable Built Environment
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Towards a Concrete Implementation of the Principle of Transparency in the Digital Services Act
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Tedarik Zinciri Bakış Açısıyla Şirketlerin Yeşil Aklama ile Başa Çıkma Stratejileri
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Large Language Models and the Gaming Problem
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Novel Insights in Deep Learning for Predicting Climate Phenomena
Naisipour, M., Ganji, S., Saeedpanah, I., Mehrakizadeh, B., & Adib, A. (2024) · International Conference on Computer and Knowledge Engineering · doi:10.1109/iccke65377.2024.10874569 · Cited by 3
Methods of Intelligent Data Analysis in Hive State Assessment Problem
Dokukin, A. A., Kuznetsova, A. V., Okulov, N. V., Sen'ko, O. V., & Chuchupal, V. Y. (2024) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661824701347 · Cited by 1
“Another significant disadvantage of the majority of modern machine learning methods is the transparency of the automated classification algorithms they yield, meaning that it is difficult for the user to understand how they operate and what the learning process is like [15].”
Quantifying Truthfulness: A Probabilistic Framework for Atomic Claim-Based Misinformation Detection
Sufi, F. K., & Alsulami, M. (2025) · Mathematics · doi:10.3390/math13111778 · Cited by 1
AI-powered image standardization in microphysiological system platforms
Park, W., Kim, C., & Ko, J. (2025) · JMST Advances · doi:10.1007/s42791-025-00112-5 · Cited by 1
“The absence of common reference datasets limits opportunities for algorithm benchmarking, performance validation, and reproducible research practices [114].”
Gastounioti, A., & Conant, E. F. (2020) · American Journal of Roentgenology · doi:10.2214/ajr.20.25196 · Cited by 1
Tinkering: A Way Towards Designing Transparent Algorithmic User Interfaces.
Showkat, D. (2021) · IUI Workshops · Cited by 1
Saraçoğlu, B. Ö. (2022) · Journal of Energy Systems · doi:10.30521/jes.1021838 · Cited by 1
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
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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