Algorithms, Air Pollution, and Adverse Outcome Pathways: Leveraging Artificial Intelligence and Machine Learning to Advance Environmental Health Research and Decisions

Posted on

June 6-7, 2019

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

This event is free to attend and will be webcast live on our home page

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).

Scientists are also beginning to apply these advanced, emerging technologies to environmental health research in a variety of ways such as characterizing sources of pollution, predicting chemical toxicity, estimating human exposures to contaminants, and identifying health outcomes.  Although these applications show promise, questions remain about the use of artificial intelligence and machine learning in environmental health research and public policy decisions.  Fundamental issues of data availability, quality, bias, and uncertainty in the data used to develop machine learning algorithms are compounded by lack of transparency and interpretability of artificial intelligence systems. These issues can impact the replicability of results, deliver misleading or inaccurate results, and potentially diminish trust in artificial intelligence systems.

Join the National Academies’ standing committee on the Use of Emerging Science for Environmental Health Decisions to
explore the promise and challenges faced in applying artificial intelligence to environmental health research and decision making.


If you have any questions for the event organizers or would like to request accommodations, please contact Jessica De Mouy at


This National Academies of Sciences, Engineering, and Medicine activity is sponsored by the National Institute of Environmental Health Sciences (NIEHS).


Meeting Materials


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.


Staff Leads


Join the conversation on Twitter with #ESEHDWorkshop