Tackling Climate Change with Machine Learning: Opportunities, Challenges, and Considerations
A SDM 2023 Tutorial
Venue: Graduate Minneapolis Hotel | Minneapolis, Minnesota, U.S. (co-located with SDM 2023)
Date: TBA
Tutorial Description
Climate change is one of the greatest challenges that society faces today, requiring rapid action from across society. Addressing climate change involves mitigation (reducing emissions) and adaptation (preparing for unavoidable consequences). In recent years, machine learning (ML) has been recognized as a broadly powerful tool for technological progress and can play an impactful role in many broader strategies for reducing and responding to climate change. At the same time, machine learning is not a silver bullet, and should serve to supplement (rather than divert attention from) other impactful actions to address climate change.
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.
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.
Tutorial Outline
- Introduction to climate change [20 mins]
- The state of climate change and climate science
- Approaches for mitigation and adaptation
- Opportunities for ML in climate action [20 mins]
- Discussion of key challenges that ML can help address
- Selected research challenges [40 mins]
- Physics-informed and robust ML
- Interpretable ML and uncertainty quantification
- Generalization and causality
- Is ML a help or hindrance to climate action? [20 mins]
- Carbon footprint of ML and system level impacts
- Considerations for research and deployment [10 mins]
- Pathway to impact and stakeholder engagement
- Takeaways and how to get involved [10 mins]
Presenters and Contributors
Presenters
Hari Prasanna Das (UC Berkeley)
Utkarsha Agwan (UC Berkeley)
Contributors
Priya L. Donti (Climate Change AI)
David Rolnick (McGill, Mila)
Lynn H. Kaack (Hertie School)
Climate Change AI team
Relevant Material
- Rolnick, D., Donti, P.L., Kaack, L.H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A.S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A. and Luccioni, A.S., 2022. Tackling climate change with machine learning. ACM Computing Surveys (CSUR), 55(2), pp.1-96. Link: https://arxiv.org/abs/1906.05433