Year: 2023
Type: preprint
Abstract: In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-21, 2022. We identify key open research questions in resource allocation, planning, and interventions for biodiversity conservation, highlighting conservation challenges that not only require AI solutions, but also require novel methodological advances. In addition to providing a summary of the workshop talks and discussions, we hope this document serves as a call-to-action to orient the expansion of algorithmic decision-making approaches to prioritize real-world conservation challenges, through collaborative efforts of ecologists, conservation decision-makers, and AI researchers.
Source: arXiv (Cornell University)
Authors: Xu, Lily, Rolf, Esther, Beery, Sara, Bennett, Joseph R., Berger-Wolf, Tanya, Birch, Tanya, Bondi-Kelly, Elizabeth, Brashares, Justin, Chapman, Melissa, Corso, Anthony, Davies, Andrew, Garg, Nikhil, Gaylard, Angela, Heilmayr, Robert, Kerner, Hannah, Klemmer, Konstantin, Kumar, Vipin, Mackey, Lester, Monteleoni, Claire, Moorcroft, Paul, Palmer, Jonathan, Perrault, Andrew, Thau, David, Tambe, Milind
Institutions: Harvard University, Massachusetts Institute of Technology, Carleton University, Google (United States), University of Michigan, University of California, Berkeley, Vaughn College of Aeronautics and Technology, American Institute of Aeronautics and Astronautics, Stanford University, Nanjing University of Aeronautics and Astronautics, Cornell Tech, University of California, Santa Barbara, Arizona State University, Microsoft (United States), University of Minnesota, Institut national de recherche en sciences et technologies du numérique, University of Colorado Boulder, Wildlife Conservation Society, The Ohio State University, World Wildlife Fund
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Life on land
Open Access status: green
OpenAlex ID: W4384807663