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.
This workshop will explore emerging applications of AI and machine learning in environmental health research. Speakers will highlight 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 will examine 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 will also discuss how these issues may impact the replicability of results, deliver misleading or inaccurate results, and potentially diminish social trust in research.