Identifying and categorizing bias in AI/ML for Earth sciences
McGovern, A., Bostrom, A., McGraw, M., Chase, R. J., Gagne, D. J., et al. (2024). Identifying and categorizing bias in AI/ML for Earth sciences. Bulletin of the American Meteorological Society, doi:10.1175/BAMS-D-23-0196.1
| Title | Identifying and categorizing bias in AI/ML for Earth sciences |
|---|---|
| Author(s) | Amy McGovern, Ann Bostrom, Marie McGraw, Randy J. Chase, David John Gagne, Imme Ebert-Uphoff, Kate D. Musgrave, Andrea Schumacher |
| Abstract | Artificial intelligence (AI) can be used to improve performance across a wide range of Earth system prediction tasks. As with any application of AI, it is important for AI to be developed in an ethical and responsible manner to minimize bias and other effects. In this work, we extend our previous work demonstrating how AI can go wrong with weather and climate applications by presenting a categorization of bias for AI in the Earth sciences. This categorization can assist AI developers to identify potential biases that can affect their model throughout the AI development life cycle. We highlight examples from a variety of Earth system prediction tasks of each category of bias. |
| Publication Title | Bulletin of the American Meteorological Society |
| Publication Date | Mar 1, 2024 |
| Publisher's Version of Record | https://dx.doi.org/10.1175/BAMS-D-23-0196.1 |
| OpenSky Citable URL | ark:/85065/d77948vf |
| OpenSky Listing | View on OpenSky |
| CISL Affiliations | TDD, MILES |