Dr. F.C. Kohli Centre of ExcellencePerspectives in Mathematical SciencesJanuary 10–February 4, 2022Wednesday, 19 January 2022, 19:30 ISTCynthia Rudin, Duke UniversityTitle Interpretable Machine Learning for High-Stakes Decisions (Video Recording) Abstract 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. About the speaker
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Cynthia Rudin is a professor of computer science, electrical
and computer engineering, statistical science, and
biostatistics & bioinformatics at Duke University, and directs
the Interpretable Machine Learning Lab. 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 (AAAI). She is also a three-time winner
of the INFORMS Innovative Applications in Analytics Award, was
named as one of the "Top 40 Under 40" by Poets and Quants in
2015, and was named by Businessinsider.com as one of the 12
most impressive professors at MIT in 2015. She is a fellow of
the American Statistical Association and a fellow of the
Institute of Mathematical Statistics.
