Getting real about synthetic data ethics
Author:
Publisher
Springer Science and Business Media LLC
Link
https://www.embopress.org/doi/epdf/10.1038/s44319-024-00101-0
Reference19 articles.
1. Achuthan S, Chatterjee R, Kotnala S, Mohanty A, Bhattacharya S, Salgia R, Kulkarni P (2022) Leveraging deep learning algorithms for synthetic data generation to design and analyze biological networks. J Biosci 47:43
2. D’Amico S, Dall’Olio D, Sala C, Dall’Olio L, Sauta E, Zampini M, Asti G, Lanino L, Maggioni G, Campagna A et al (2023) Synthetic data generation by artificial intelligence to accelerate research and precision medicine in hematology. JCO Clin Cancer Inform 7:e2300021
3. DeCamp M, Lindvall C (2023) Mitigating bias in AI at the point of care. Science 381:150–152
4. Gebru T, Morgenstern J, Vecchione B, Vaughan J, Wortman, Wallach H, Iii HD, Crawford K (2021) Datasheets for datasets. Commun ACM 64:86–92
5. Gero KI, Das P, Dognin P, Padhi I, Sattigeri P, Varshney KR (2023) The incentive gap in data work in the era of large models. Nat Mach Intell 5:565–567
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