Developing ethical, responsible and trustworthy AI approaches for environmental science

Environmental Data Science (2022). DOI: 10.1017/eds.2022.5″ width=”800″ height=”434″/>

Humans sometimes create conflicting data, which can be ingested by an artificial intelligence (AI) model (Section 2.1.3). While in example (a) the user’s intent is to create false entries, in example (b) the motivation for the false reports is more personal financial gain (insurance fraud). However, both can cause problems for AI by directly affecting the reporting databases used by AI models. Credit: Environmental Data Science (2022). DOI: 10.1017/eds.2022.5

There is no doubt that artificial intelligence is ingrained in our daily lives. From smartphones to ride-sharing apps to mobile check deposits, AI is so pervasive that we rarely think about how it works.

For a University of Oklahoma scientist, however, artificial intelligence and machine learning are at the forefront of her work, especially when it comes to weather. Amy McGovern, Ph.D., directs the National Science Foundation AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography at the University of Oklahoma.

An American meteorology fellow, McGovern has studied severe weather since the late 1990s. Over the course of her career, she has witnessed a rapid emergence in the field of AI, while developing what she hopes will be reliable AI methods to avoid weather and climate disasters.

Lately, however, McGovern and researchers in Colorado and Washington have noticed serious disparities in AI, noting that the methods are biased, especially when it comes to geodiversity.

“The artificial intelligence algorithms are based on mathematical formulas which are considered objective; however, there is a bias towards high population areas, as well as wealthier areas,” said McGovern, a professor at the school. of IT from the OU. of Meteorology.

“For example, if more people live in an area, there is a higher chance of someone observing and reporting a hail or tornado event. This can bias the AI ​​model to over-predict hail and storms. tornadoes in urban areas and under-predict severe weather in rural towns,” she said.

AI tools, whether predicting hail, wind or tornadoes, are supposed to be inherently objective. They are not, says McGovern.

Raising awareness

The team recently authored an article titled “Why We Need to Focus on Developing Ethical, Accountable, and Trustworthy AI Approaches for Environmental Science.” Published by Cambridge University Press, the article will appear in the first issue of Environmental Data Science.

Researchers are exploring ethical AI methods, particularly in the field of environmental science. “Whether involved in education, industry or government, environmental scientists are absolutely essential to developing meaningful AI tools, and more educational resources are needed to help environmental scientists environment to learn the basics of artificial intelligence so that they can play a leading role in future developments,” McGovern said.

The group sees the ethics of AI in environmental science as an emerging trend in education. “With the rapid emergence of data science techniques in science 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 unwittingly do more harm than good if not developed and applied responsibly, McGovern says. “We hope our work is a major step towards making more ethically informed AI systems in environmental science.”


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More information:
Amy McGovern et al, Why We Need to Focus on Developing Ethical, Accountable, and Trustworthy Artificial Intelligence Approaches to Environmental Science, Environmental Data Science (2022). DOI: 10.1017/eds.2022.5

Provided by the University of Oklahoma


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