Abstract: The 2003 U.S. Supreme Court case known as Gratz v. Bollinger addresses a formula for college admissions that was created by a graduate student in statistics at the University of Michigan for a specific purpose. The method used to create this formula is considered simplistic by modern machine learning standards; yet the debate that ensued, which could not have happened if a more modern approach had been used, illustrates that science and society do not always benefit from machine learning models that achieve the best possible predictive performance. This talk discusses the history of the legal case, the admissions formula and how it was created, and the implications of the debate for how we build predictive models.


David R. Hunter is a Professor of Statistics at Penn State. He earned his Ph.D. in statistics from the University of Michigan in 1999. He has been at Penn State University since 1999, where he is Professor of Statistics and served as head of the Department of Statistics from 2012 to 2018.

Hunter has published widely on statistical models for networks and is a co-creator of the "statnet" suite of packages for network analysis in R. He co-coined the term "MM algorithms" and has written extensively on this and other EM-like algorithms. He has also extended the theory and computational practice of unsupervised clustering using nonparametric finite mixture models.

https://science.psu.edu/stat/people/drh20