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Responsible AI for conservation

Nature Machine Intelligence volume 1, pages 72–73 (2019)Cite this article

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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.

Machine learning (ML) is revolutionizing efforts to conserve nature. ML algorithms are being applied to predict the extinction risk of thousands of species1, assess the global footprint of fisheries2, and identify animals and humans in wildlife sensor data recorded in the field3. These efforts have recently been given a huge boost with support from the commercial sector. New initiatives, such as Microsoft’s AI for Earth4 and Google’s AI for Social Good, are bringing new resources and new ML tools to bear on some of the biggest challenges in conservation. In parallel to this, the open data revolution means that global-scale, conservation-relevant datasets can be fed directly to ML algorithms from open data repositories, such as Google Earth Engine for satellite data5 or Movebank for animal tracking data6. Added to these will be Wildlife Insights, a Google-supported platform for hosting and analysing wildlife sensor data that launches this year. With new tools and a proliferation of data comes a bounty of new opportunities, but also new responsibilities.

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

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Authors and Affiliations

  1. Institute of Zoology, Zoological Society of London, London, UK

    Oliver R. Wearn, Robin Freeman & David M. P. Jacoby

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  1. Oliver R. Wearn
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  2. Robin Freeman
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  3. David M. P. Jacoby
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Correspondence to Oliver R. Wearn or David M. P. Jacoby.

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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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