Detection and attribution of biodiversity change: a role for AI
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Understanding 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.
This live event includes a 30-minute networking event hosted on the AI for Good Neural Network. This is your opportunity to ask questions, interact with the panelists and participants and build connections with the AI for Good community.