Leveraging Artificial Intelligence and Machine Learning to Advance Environmental Health Research and Decisions

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June 6-7, 2019

National Academies of Sciences, Engineering, and Medicine
Keck Center, Room 100
500 5th Avenue NW
Washington, DC 20001

Meeting Details

Session 4 – Hands-On Machine Learning Demo Materials

Artificial Intelligence is being called the new electricity—a technological invention that promises to transform our lives and the world.  The resurgence of investment and enthusiasm for artificial intelligence, or the ability of machines to carry out “smart” tasks, is 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. Driven by tremendous growth in data collection and availability as well as computing power and accessibility, artificial intelligence and machine learning applications are rapidly growing in society, including retail (e.g., predicting consumer purchases), the automotive industry (e.g., self-driving cars), and health care (e.g., automated medical diagnoses).

This workshop explored emerging applications and implications of AI and machine learning in environmental health research. Speakers highlighted the use of AI and machine learning to characterize sources of pollution, predict chemical toxicity, estimate human exposures to contaminants, and identify health outcomes, among other applications.   Although these applications show promise, questions remain about the use of AI and machine learning in environmental health research and public policy decisions.  Workshop participants examined how fundamental issues about data availability, quality, bias, and uncertainty in the data used to develop machine learning algorithms are compounded by lack of transparency and interpretability of AI systems. Participants also discussed how these issues may impact the reproducibility and replicability of results, deliver misleading or inaccurate results, and potentially diminish social trust in research. The workshop featured a hands-on learning session to engage scientists and decision makers in these important, cross-disciplinary issues.

This event was made possible by the generous support of the National Institute of Environmental Health Sciences.


Workshop organizing committee: Kevin Elliott, Michigan State University; Nicole Kleinstreuer, National Institutes of Health; Patrick McMullen, ScitoVation; Gary Miller, Columbia University; Bhramar Mukherjee, University of Michigan; Roger D. Peng, Johns Hopkins University; Melissa Perry, The George Washington University; Reza Rasoulpour, Corteva Agriscience

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