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ArticleIdentifying and Categorizing Bias in AI/ML for Earth Sciences Amy McGovern, Ann Bostrom, Marie McGraw, Randy J. Chase, David John Gagne II, Imme EbertUphoff, Kate D. Musgrave, and Andrea SchumacherKEYWORDS: Atmosphere; Ocean; Artificial intelligence; Other artificial intelligence/machine learningABSTRACT: 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...
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Review the content or argument to identify potential biases.
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Examine the sources of information used to determine if they are credible and unbiased.
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Categorize the type of bias, such as confirmation bias, implicit bias, or systemic bias.
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Identifying and categorizing bias involves recognizing and classifying different types of biases that can affect decision-making, opinions, and perceptions. This process is crucial in various fields to ensure fairness, accuracy, and integrity.
Individuals or organizations that are involved in research, reporting, or decision-making processes where bias could influence outcomes are typically required to file identifying and categorizing bias. This often includes researchers, journalists, and regulatory bodies.
To fill out identifying and categorizing bias, one should first recognize the instances of bias that occur, categorize them according to predefined types, collect relevant data or evidence, and then document this information in a structured format as per guidelines.
The purpose of identifying and categorizing bias is to promote transparency, enhance the validity of conclusions, and mitigate the effects of bias in judgments and decisions. It aims to ensure a more balanced and accurate representation of information.
The information that must be reported includes the type of bias identified, the context in which it occurred, the impact or influence of the bias, and any measures taken to address or mitigate it. Specific reporting formats may vary based on the guidelines followed.
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