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.
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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).
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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
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DOI: https://doi.org/10.1038/s42256-019-0022-7
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