Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Responsible AI for conservation

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Machine learning algorithms on the front line of conservation.


  1. Darrah, S. E., Bland, L. M., Bachman, S. P., Clubbe, C. P. & Trias-Blasi, A. Divers. Distrib. 23, 435–447 (2017).

    Article  Google Scholar 

  2. Kroodsma, D. A. et al. Science 908, 904–908 (2018).

    Article  Google Scholar 

  3. Mac Aodha, O. et al. PLoS Comput. Biol. 14, e1005995 (2018).

    Article  Google Scholar 

  4. Joppa, L. N. Nature 552, 325–328 (2017).

    Article  Google Scholar 

  5. Gorelick, N. et al. Remote Sens. Environ. 202, 18–27 (2017).

    Article  Google Scholar 

  6. Kranstauber, B. et al. Environ. Model. Softw. 26, 834–835 (2011).

    Article  Google Scholar 

  7. Amodei, D. et al. Preprint at (2016).

  8. Collar, N. J. Oryx 32, 239–243 (1998).

    Article  Google Scholar 

  9. Doshi-Velez, F. & Kim, B. Preprint at (2017).

  10. Tabak, M. A. et al. Methods Ecol. Evol. (2018).

    Article  Google Scholar 

  11. Norouzzadeh, M. S. et al. Proc. Natl Acad. Sci. USA 115, E5716–E5725 (2018).

    Article  Google Scholar 

  12. 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).

  13. Reed, J. M. et al. Conserv. Biol. 16, 7–19 (2002).

    Article  Google Scholar 

  14. 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).

  15. Crawford, K. & Calo, R. T. Nature 538, 311–313 (2016).

    Article  Google Scholar 

  16. Zou, J. & Schiebinger, L. Nature 559, 324–326 (2018).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding authors

Correspondence to Oliver R. Wearn or David M. P. Jacoby.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wearn, O.R., Freeman, R. & Jacoby, D.M.P. Responsible AI for conservation. Nat Mach Intell 1, 72–73 (2019).

Download citation

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing