Name
Cynthia Rudin, Ph.D., Professor of Computer Science, Duke University
Associated Research
interpretable modeling, interpretable policy design, variable importance measures,
causal inference methods, uncertainty quantification, and domain knowledge-based machine learning
Talk title (tentative)
Interpretable Machine Learning: Bringing Data Science out of the “Dark Age”
Abstract (tentative)
With widespread use of machine learning, there have been serious societal consequences from using black box models for high-stakes decisions in criminal justice, healthcare, financial lending, and beyond. Interpretability of machine learning models is critical when the cost of a wrong decision is high. Throughout my career, I have had the opportunity to work with power engineers, doctors, and police detectives. Using interpretable models has been the key to allowing me to help them with important high-stakes societal problems. Interpretability can bring us out of the “dark” age of the black box into the age of insight and enlightenment.
Short Bio
Cynthia Rudin is a professor of computer science and engineering at Duke University. She directs the Interpretable Machine Learning Lab, and her goal is to design predictive models that people can understand. Her lab applies machine learning in many areas, such as healthcare, criminal justice, and energy reliability. She holds degrees from the University at Buffalo and Princeton. She is the recipient of the 2022 Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity from the Association for the Advancement of Artificial Intelligence (the “Nobel Prize of AI”). She received a 2022 Guggenheim fellowship, and is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the Association for the Advancement of Artificial Intelligence. Her work has been featured in many news outlets including the NY Times, Washington Post, Wall Street Journal, and Boston Globe.