• Clearing up biases in artificial intelli

    From ScienceDaily@1:317/3 to All on Wednesday, April 20, 2022 22:30:48
    Clearing up biases in artificial intelligence
    Group's goal is to help environmental scientists learn the basics of AI


    Date:
    April 20, 2022
    Source:
    University of Oklahoma
    Summary:
    Scientists have noticed grave disparities in artificial
    intelligence, noting that the methods are not objective, especially
    when it comes to geodiversity. AI tools, whether forecasting hail,
    wind or tornadoes, are assumed to be inherently objective, says
    one of the researchers. They aren't, she says.



    FULL STORY ========================================================================== There's no doubt that artificial intelligence is embedded in our everyday lives. From smartphones to ridesharing apps to mobile check deposits,
    AI is so pervasive that we rarely think about how it works.


    ==========================================================================
    For one University of Oklahoma scientist, however, artificial intelligence
    and machine learning are at the forefront of her work -- expressly as
    it relates to weather. Amy McGovern, Ph.D., leads the National Science Foundation AI Institute for Research on Trustworthy AI in Weather,
    Climate, and Coastal Oceanography at the University of Oklahoma.

    An American Meteorological Fellow, McGovern has been studying severe
    weather phenomena since the late 1990s. During her career, she has
    witnessed a rapid emergence in the AI field, all while developing what she hopes are trustworthy AI methods to avert weather and climate disasters.

    Lately, however, McGovern and researchers from Colorado and Washington
    have noticed grave disparities in AI, noting that the methods are not objective, especially when it comes to geodiversity.

    "Artificial intelligence algorithms are based on mathematical formulas
    that are seen as objective; however, there is a bias toward areas with
    higher populations, as well as areas that are more affluent," said
    McGovern, a professor at OU's School of Computer Science and School
    of Meteorology.

    "For example, if more people live in an area, there is a higher chance
    that someone observes and reports a hail or tornado event. This can
    bias the AI model to over-predict hail and tornadoes in urban areas and under-predict severe weather in rural towns," she said.

    AI tools, whether forecasting hail, wind or tornadoes, are assumed to
    be inherently objective. They aren't, McGovern says.

    Raising Awareness The team recently published a paper titled "Why We Need
    to Focus on Developing Ethical, Responsible, and Trustworthy AI Approaches
    for Environmental Sciences." Published by Cambridge University Press, the
    paper will appear in the inaugural issue of Environmental Data Science.

    The researchers are exploring ethical AI methods, specifically in the
    field of environmental sciences. "Whether involved in teaching, industry
    or government, environmental scientists are absolutely essential for
    developing meaningful AI tools, and more educational resources are
    needed to help environmental scientists learn the basics of artificial intelligence so they can play a leading role in future developments,"
    McGovern said.

    The group sees ethics in AI in the environmental sciences as an emerging
    trend in education. "With the rapid emergence of data science techniques
    in the sciences and the societal importance of many of these applications, there is an urgent need to prepare future scientists to be knowledgeable," McGovern said.

    AI systems can be as flawed as the people who create them and can unintentionally do more harm than good if not developed and applied responsibly, McGovern says. "We hope our work is a major step toward
    making AI systems more ethically informed in environmental science."

    ========================================================================== Story Source: Materials provided by University_of_Oklahoma. Note:
    Content may be edited for style and length.


    ========================================================================== Journal Reference:
    1. Amy McGovern, Imme Ebert-Uphoff, David John Gagne, Ann Bostrom. Why
    we
    need to focus on developing ethical, responsible, and trustworthy
    artificial intelligence approaches for environmental science.

    Environmental Data Science, 2022; 1 DOI: 10.1017/eds.2022.5 ==========================================================================

    Link to news story: https://www.sciencedaily.com/releases/2022/04/220420133607.htm

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