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Artificial intelligence (AI) is a technological invention that promises to transform everyday life and the world. Investment and enthusiasm for AI—or the ability of machines to carry out “smart” tasks—are driven largely by advancements in the subfield of machine learning. Machine learning algorithms can analyze large volumes of complex data to find patterns and make predictions, often exceeding the accuracy and efficiency of people who are attempting the same task. Powered by a tremendous growth in data collection and availability as well as computing power and accessibility, AI and machine learning applications are becoming commonplace in many aspects of modern society, as well as in a growing number of scientific disciplines.
On June 6–7, 2019, the National Academies of Sciences, Engineering, and Medicine held a 2-day workshop to explore emerging applications and implications of AI and machine learning in environmental health research and decisions. Speakers highlighted the use of AI and machine learning to characterize sources of pollution, predict chemical toxicity, and estimate human exposures to contaminants, among other applications. Though promising, questions remain about the use of AI and machine learning in environmental health research and public policy decisions. This publication summarizes the presentations and discussions from the workshop.
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