Identifying and categorizing bias in AI/ML for Earth sciences
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.
document
https://n2t.org/ark:/85065/d77948vf
eng
geoscientificInformation
Text
publication
2016-01-01T00:00:00Z
publication
2024-03-01T00:00:00Z
Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
None
OpenSky Support
UCAR/NCAR - Library
PO Box 3000
Boulder
80307-3000
name: homepage
pointOfContact
OpenSky Support
UCAR/NCAR - Library
PO Box 3000
Boulder
80307-3000
name: homepage
pointOfContact
2025-07-10T20:03:52.786966