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Climate Change and Machine Learning: Opportunities, Challenges, and Considerations
Priya Donti · David Rolnick · Lynn Kaack

Mon Jul 18 10:00 AM -- 12:00 PM (PDT) @ Room 307

Climate change is one of the greatest challenges that society faces today, requiring rapid action from across society. In this tutorial, we will provide an introduction to climate change, what it means to address it, and how machine learning can play a role. From energy to agriculture to disaster response, we will describe high-impact problems where machine learning can help, e.g., by providing decision-relevant information, optimizing complex systems, and accelerating scientific experimentation. These problems encompass exciting opportunities for both methodological innovation and on-the-ground implementation. We will also describe avenues for machine learning researchers and practitioners to get involved, alongside key considerations for the responsible development and deployment of such work. While this tutorial will primarily discuss opportunities for machine learning to help address climate change, it is worth noting that machine learning is a general-purpose technology that can be used for applications that both help and hinder climate action. In addition, machine learning has its own computational and hardware footprint. We will therefore briefly present a framework for understanding and contextualizing machine learning’s overall climate impacts, and describe associated considerations for machine learning research and practice as a whole. Through the course of this tutorial, we hope that participants will gain a deeper understanding of how climate change and machine learning intersect, as well as how they can get involved by using their skills to help address the climate crisis.

Author Information

Priya Donti (Carnegie Mellon University)
David Rolnick (McGill University, Mila)
Lynn Kaack (Hertie School)

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