Artificial intelligence (AI) promises to be an invaluable tool for nature conservation, but its misuse could have severe real-world consequences for people and wildlife. Conservation scientists discuss how improved metrics and ethical oversight can mitigate these risks.
This is a preview of subscription content, access via your institution
Access options
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
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Darrah, S. E., Bland, L. M., Bachman, S. P., Clubbe, C. P. & Trias-Blasi, A. Divers. Distrib. 23, 435–447 (2017).
Kroodsma, D. A. et al. Science 908, 904–908 (2018).
Mac Aodha, O. et al. PLoS Comput. Biol. 14, e1005995 (2018).
Joppa, L. N. Nature 552, 325–328 (2017).
Gorelick, N. et al. Remote Sens. Environ. 202, 18–27 (2017).
Kranstauber, B. et al. Environ. Model. Softw. 26, 834–835 (2011).
Amodei, D. et al. Preprint at https://arxiv.org/abs/1606.06565 (2016).
Collar, N. J. Oryx 32, 239–243 (1998).
Doshi-Velez, F. & Kim, B. Preprint at https://arxiv.org/abs/1702.08608 (2017).
Tabak, M. A. et al. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.13120 (2018).
Norouzzadeh, M. S. et al. Proc. Natl Acad. Sci. USA 115, E5716–E5725 (2018).
Burgman, M. & Possingham, H. P. in Genetics, Demography and Viability of Fragmented Populations (eds Young, A. G. & Clarke, G. M.) 97–112 (Cambridge Univ. Press, Cambridge, 2000).
Reed, J. M. et al. Conserv. Biol. 16, 7–19 (2002).
Ralls, K., Beissinger, S. R. & Cochrane, J. F. in Population Viability Analysis (eds Beissinger, S. R. & McCullough, D. R.) 521–550 (Univ. Chicago Press, Chicago, 2002).
Crawford, K. & Calo, R. T. Nature 538, 311–313 (2016).
Zou, J. & Schiebinger, L. Nature 559, 324–326 (2018).
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Rights and permissions
About this article
Cite this article
Wearn, O.R., Freeman, R. & Jacoby, D.M.P. Responsible AI for conservation. Nat Mach Intell 1, 72–73 (2019). https://doi.org/10.1038/s42256-019-0022-7
Published:
Issue Date:
DOI: https://doi.org/10.1038/s42256-019-0022-7
This article is cited by
-
Responsible Artificial Intelligence as a Secret Ingredient for Digital Health: Bibliometric Analysis, Insights, and Research Directions
Information Systems Frontiers (2023)
-
Responsible Artificial Intelligence in Healthcare: Predicting and Preventing Insurance Claim Denials for Economic and Social Wellbeing
Information Systems Frontiers (2023)
-
Responsible AI for Digital Health: a Synthesis and a Research Agenda
Information Systems Frontiers (2023)
-
The Application of the Principles of Responsible AI on Social Media Marketing for Digital Health
Information Systems Frontiers (2023)
-
Deep learning in terrestrial conservation biology
Biologia Futura (2023)