Explainable AI for the Geosciences Speaker: Elizabeth A. Barnes

Recent advances in machine learning have yielded many breakthroughs in commercial applications, and these techniques hold enormous promise for scientific discovery. While exciting advances with these tools have already been seen in other scientific disciplines, e.g. life sciences, they have been more slowly embraced by the geoscience community. One possible explanation for this is the perceived "black box" that outputs an answer without any explanation as to "why?" or "how?".

In this talk, I will discuss how the field can make the most of machine learning interpretation techniques (i.e. "explainable AI") to open the black box and push the bounds of scientific discovery. This has profound implications for machine learning use in science, as it not only increases trust in the output, but also allows us to learn new science from the decision making process of the algorithm itself. I will discuss applications in climate science, including subseasonal-to-decadal prediction, the atmospheric response to climate change, and anthropogenic impacts on Earth's surface. While these examples are focused on climate problems, the tools and the approach are widely applicable and offer an exciting path for the future of geoscientific research.