The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Artificial intelligence and statistics
Open accessAbstract
Artificial intelligence (AI) is intrinsically data-driven. It calls for the application of statistical concepts through human-machine collaboration during the generation of data, the development of algorithms, and the evaluation of results. This paper discusses how such human-machine collaboration can be approached through the statistical concepts of population, question of interest, representativeness of training data, and scrutiny of results (PQRS). The PQRS workflow provides a conceptual framework for integrating statistical ideas with human input into AI products and researches. These ideas include experimental design principles of randomization and local control as well as the principle of stability to gain reproducibility and interpretability of algorithms and data results. We discuss the use of these principles in the contexts of self-driving cars, automated medical diagnoses, and examples from the authors’ collaborative research.
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Cites
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Iterative random forests to discover predictive and stable high-order interactions
Basu, S., Kumbier, K., Brown, J. B., & Yu, B. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1711236115 · Cited by 317
Self-citationYu, B. (2013) · Bernoulli · doi:10.3150/13-bejsp14 · Cited by 113
Self-citationRNF169 limits 53BP1 deposition at DSBs to stimulate single-strand annealing repair
An, L., Dong, C., Li, J., Chen, J., Yuan, J., Huang, J., Chan, K. M., Yu, C., & Huen, M. S. Y. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1804823115 · Cited by 69
Cited by
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., et al. (2019) · arXiv · doi:10.48550/arxiv.1910.10045 · Cited by 11,321
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., et al. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.008 · Cited by 3,111
A survey on federated learning
Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., & Gao, Y. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106775 · Cited by 1,899
Self-citationAutomated machine learning: Review of the state-of-the-art and opportunities for healthcare
Waring, J., Lindvall, C., & Umeton, R. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101822 · Cited by 834
Metadata
- Authors
- Yu, B., & Kumbier, K.
- Venue
- Frontiers of Information Technology & Electronic Engineering
- Publisher
- Zhejiang University Press
- Published
- 2018-01
- Volume
- 19
- Issue
- 1
- Pages
- 6-9
- ISSN
- 2095-9184 (ISSN-L)
- 2095-9230
- OpenAlex
- W2963819344
- Cited by
- 2,329
- References
- 8
- FWCI
- 49.58
- Type
- journal article
- Language
- en
- Open access
- green
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