Fairness, Accountability, and Transparency: (Counter)-Examples from Predictive Models in Criminal Justice

When and Where

Monday, October 26, 2020 3:00 pm to 4:00 pm


Kristian Lum, University of Pennsylvania


The need for fairness, accountability, and transparency in computer models that make or inform decisions about people has become increasingly clear over the last several years. One application area where these topics are particularly important is criminal justice, as statistical models are being used to make or inform decisions that impact highly consequential decisions— those concerning an individual’s freedom. In this talk, I’ll highlight three threads of my own research into the use of machine learning and a statistical models in criminal justice models that demonstrate the importance of careful attention to fairness, accountability, and transparency. In particular, I’ll discuss how predictive policing has the potential to reinforce and amplify unfair policing practices of the past. I’ll also discuss some of the ways in which recidivism prediction models can fail to require the accountability and transparency necessary to prevent gaming.

Please Join the event.

About Kristian Lum

Kristian Lum is a Research Assistant Professor in the Department of Computer and Information Science at University of Pennsylvania. Previously, she worked as the Lead Statistician at the Human Rights Data Analysis Group (HRDAG), where she leads the HRDAG project on criminal justice in the United States.

Kristian’s research primarily focuses on examining the uses of machine learning in the criminal justice system and has concretely demonstrated the potential for machine learning-based predictive policing models to reinforce and, in some cases, amplify historical racial biases in law enforcement. 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 incorporate fairness considerations and advancing HRDAG’s core statistical methodology—record-linkage and capture-recapture methods for estimating the number of undocumented conflict casualties.

She is the primary author of the dga package, open source software for population estimation for the R computing environment.

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.