Cargando…
Public attitudes value interpretability but prioritize accuracy in Artificial Intelligence
As Artificial Intelligence (AI) proliferates across important social institutions, many of the most powerful AI systems available are difficult to interpret for end-users and engineers alike. Here, we sought to characterize public attitudes towards AI interpretability. Across seven studies (N = 2475...
Autores principales: | Nussberger, Anne-Marie, Luo, Lan, Celis, L. Elisa, Crockett, M. J. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9528860/ https://www.ncbi.nlm.nih.gov/pubmed/36192416 http://dx.doi.org/10.1038/s41467-022-33417-3 |
Ejemplares similares
-
Interpretable artificial intelligence in radiology and radiation oncology
por: Cui, Sunan, et al.
Publicado: (2023) -
Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation
por: Das, Nilakash, et al.
Publicado: (2023) -
An empirical bioethical examination of Norwegian and British doctors' views of responsibility and (de)prioritization in healthcare
por: Everett, Jim A. C., et al.
Publicado: (2021) -
Diagnostic accuracy of the PMcardio smartphone application for artificial intelligence–based interpretation of electrocardiograms in primary care (AMSTELHEART-1)
por: Himmelreich, Jelle C.L., et al.
Publicado: (2023) -
Artificial intelligence in medical referrals triage based on Clinical Prioritization Criteria
por: Abdel-Hafez, Ahmad, et al.
Publicado: (2023)