Amy McGovern, Ann Bostrom, Marie McGraw, Randy J. Chase, David John Gagne, Imme Ebert-Uphoff, Kate D. Musgrave, Andrea Schumacher
{"title":"Identifying and Categorizing Bias in AI/ML for Earth Sciences","authors":"Amy McGovern, Ann Bostrom, Marie McGraw, Randy J. Chase, David John Gagne, Imme Ebert-Uphoff, Kate D. Musgrave, Andrea Schumacher","doi":"10.1175/bams-d-23-0196.1","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":9464,"journal":{"name":"Bulletin of the American Meteorological Society","volume":"58 1","pages":""},"PeriodicalIF":6.9000,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bulletin of the American Meteorological Society","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1175/bams-d-23-0196.1","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍：
The Bulletin of the American Meteorological Society (BAMS) is the flagship magazine of AMS and publishes articles of interest and significance for the weather, water, and climate community as well as news, editorials, and reviews for AMS members.
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