This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Discover Sustainability Open Access 29 November 2021
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
Vinuesa, R. et al. Nat. Commun. 11, 233 (2020).
Jean, N. et al. Science 353, 790–794 (2016).
European Commission. https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai (2019).
King, D. https://deepmind.com/blog/article/streams-and-ai (2017).
Fan, F., Xiong, J., Li, M. & Wang, G. IEEE Trans. Radiat. Plasma Med. Sci. (2021).
Rudin, C. Nat. Mach. Intell. 1, 206–215 (2019).
Jiang, C. et al. Phys. Fluids 33, 055133 (2021).
Cranmer, M. et al. NeurIPS Proceedings 33, 17429–17442 (2020).
The authors acknowledge V. Dignum for her insightful comments on this manuscript. R.V. acknowledges the financial support from the Swedish Research Council (VR).
The authors declare no competing interests.
About this article
Cite this article
Vinuesa, R., Sirmacek, B. Interpretable deep-learning models to help achieve the Sustainable Development Goals. Nat Mach Intell 3, 926 (2021). https://doi.org/10.1038/s42256-021-00414-y
This article is cited by
Nature Reviews Physics (2023)
Human-machine Collaborative Decision-making: An Evolutionary Roadmap Based on Cognitive Intelligence
International Journal of Social Robotics (2023)
On data-driven identification: Is automatically discovering equations of motion from data a Chimera?
Nonlinear Dynamics (2023)
Environment, Development and Sustainability (2023)
Nature Computational Science (2022)