Transparency and reproducibility in artificial intelligence
By Benjamin Haibe-Kains, George Adam, Ahmed Hosny, Farnoosh Khodakarami, Levi Waldron, Bo Wang, C. McIntosh, Anna Goldenberg, Anshul B Kundaje, Casey S. Greene and 12 more. Nature, 2020
429 citations recorded by the source corpus.
Breakthroughs in artificial intelligence (AI) hold enormous potential as it can automate complex tasks and go even beyond human performance. In their study, McKinney et al. showed the high potential of AI for breast cancer screening. However, the lack of methods’ details and algorithm code undermines its scientific value. Here, we identify obstacles hindering transparent and reproducible AI research as faced by McKinney et al., and provide solutions to these obstacles with implications for the broader field.
Subfields: Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/3025d34a8439ae92baca3f071d92a14f460ce8c4
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yoav Goldberg
- Byron C. Wallace
- Sarthak Jain
- Noah A. Smith
- Sarah Wiegreffe
- Ana Marasovic
- Eric P. Lehman
- Ala Al-Fuqaha
In Health Informatics
- Ricardo A. Baeza-Yates
- Ala Al-Fuqaha
- Junaid Qadir
- Byron C. Wallace
- Yoav Goldberg
- Louisa H.Y. Tsi
- Shakked Noy
- Whitney Zhang
In Radiology, Nuclear Medicine and Imaging
- Ala Al-Fuqaha
- Junaid Qadir
- Adeel Razi
- Yoav Goldberg
- Adnan Qayyum
- Shakked Noy
- Whitney Zhang
- Louisa H.Y. Tsi
Citations in the corpus
- Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2021, background): “The study was later accused of violating the principles of transparency and reproducibility [151], [152].”
- Does BERT Pretrained on Clinical Notes Reveal Sensitive Data? (2021, background): “have been cited as reasons for withholding direct publication of trained model weights (McKinney et al., 2020).”
- Harnessing multimodal data integration to advance precision oncology (2021, background): “However, a recent investigation of one of these studies 130 found that significant information needed to actually reproduce the study was missing, greatly reducing the impact and ability of the field at large to scrutinize the study 131 and improve upon it.”
- Early detection of cancer (2022, neutral): “This is an area where there is an opportunity for AI and ML to help detect cancer (114) and to predict risk of progression (115), although issues of transparency and reproducibility must be addressed (116).”
- Towards intellectual freedom in an AI Ethics Global Community (2021, background): “Problems of systemic racism and other culturally deeply encoded discriminatory mechanisms multiply the severity of such conflicts of interest insignificant, perhaps non-linear and even counter-intuitive ways [22, 24, 36, 38, 39, 44].”