October 29, 2019
3:00 PM - 4:00 PM
Title: Fairness, Accountability, and Transparency: Lessons from predictive models in criminal justice
Abstract: The related topics of fairness, accountability, and transparency in predictive modeling have seen increased attention over the last several years. One application area where these topics are particularly important is criminal justice. In this talk, I will give an overview of my work in this area, highlighting specific examples that illustrate the importance of each of these concepts.
Bio: Kristian Lum is the Lead Statistician at the Human Rights Data Analysis Group (HRDAG), where she leads the HRDAG project on criminal justice in the United States She is also a co-director of the MacArthur Foundation’s Pre-Trial Risk Management Project. Previously, Kristian worked as a research assistant professor in the Virginia Bioinformatics Institute at Virginia Tech and as the first data scientist at DataPad, a small technology start-up.
Kristian’s research primarily focuses on examining the uses of predictive modeling in the criminal justice system, including predictive policing and risk assessment. She has also applied a diverse set of methodologies to better understand the criminal justice system: causal inference methods to explore the Causal impact of setting bail on the likelihood of pleading or being found guilty; and agent-based modeling methods derived from epidemiology to study the disease-like spread of incarceration through a social influence network. Additionally, Kristian’s work encompasses the development of new statistical methods that explicitly incorperate fainess considerationsand advancing HRDAG’s core statistical methodology—record-linkage and capture-recapture methods for estimating the number of undocumented conflict casualties.
Kristian received an MS and PhD from the Department of Statistical Science at Duke University and a BA in Mathematics and Statistics from Rice University.