Detection and attribution of biodiversity change: a role for AI | AI for Biodiversity
0Understanding the pace of biodiversity change and the underlying causes for it is both of great scientific interest and central to policy efforts aimed at meeting biodiversity targets. Changes to biodiversity and the resulting ecosystem impacts are being reported worldwide. In many cases, trends in biodiversity are detected, but these trends are rarely formally attributed to possible drivers or conservation action. Professor Andrew Gonzalez, Liber Ero Chair in Biodiversity Conservation in the Department of Biology, McGill University, argues that we need a formal framework and guidelines for the detection and attribution of biodiversity change to support effective policy. He proposes an inferential framework to guide detection and attribution analyses, which identifies five steps – causal modeling, observation, estimation, detection, and attribution – for robust attribution. Artificial intelligence can play a strong role in the implementation of this framework. The framework encourages a formal and reproducible statement of confidence about the role of drivers after robust methods for biodiversity trend detection have been deployed. Confidence in trend attribution requires that data and analyses used in all steps of the framework follow best practices reducing uncertainty at each step. These steps will be illustrated with examples. This framework could strengthen the bridge between biodiversity science and artificial intelligence and therefore support rapid assessments of actions required to mitigate human impacts and reduce rates of biodiversity loss. \n\nIn partnership with: Convention on Biological Diversity\n\nSpeakers:\nDavid Cooper\nConvention on Biological Diversity\nAndrew Gonzalez\nMcGill University\nModerator(s):\nMaría Cecilia Londoño\nHumboldt Institute\nMike Gill\nNatureServe’s Biodiversity Indicators Program\n\n#AIforBiodiversity #AIforGoodDiscovery\n\nJoin the Neural Network! \nhttps://aiforgood.itu.int/neural-network/\nThe AI for Good networking community platform powered by AI. \nDesigned to help users build connections with innovators and experts, link innovative ideas with social impact opportunities, and bring the community together to advance the SDGs using AI.\n\n Watch the latest #AIforGood videos!\n\n\nExplore more #AIforGood content:\n AI for Good Top Hits\n • Top Hits \n\n AI for Good Webinars\n • AI for Good Webinars \n\n AI for Good Keynotes\n • AI for Good Keynotes \n\n Stay updated and join our weekly AI for Good newsletter:\nhttp://eepurl.com/gI2kJ5\n\n Discover what's next on our programme!\nhttps://aiforgood.itu.int/programme/\n\nCheck out the latest AI for Good news:\nhttps://aiforgood.itu.int/newsroom/\n\nExplore the AI for Good blog:\nhttps://aiforgood.itu.int/ai-for-good-blog/\n\n Connect on our social media:\nWebsite: https://aiforgood.itu.int/\nTwitter: https://twitter.com/AIforGood\nLinkedIn Page: https://www.linkedin.com/company/26511907 \nLinkedIn Group: https://www.linkedin.com/groups/8567748 \nInstagram: https://www.instagram.com/aiforgood \nFacebook: https://www.facebook.com/AIforGood\n\nWhat is AI for Good?\nWe have less than 10 years to solve the UN SDGs and AI holds great promise to advance many of the sustainable development goals and targets.\nMore than a Summit, more than a movement, AI for Good is presented as a year round digital platform where AI innovators and problem owners learn, build and connect to help identify practical AI solutions to advance the United Nations Sustainable Development Goals.\nAI for Good is organized by ITU in partnership with 40 UN Sister Agencies and co-convened with Switzerland.\n\nDisclaimer:\nThe views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.