Affiliation
Human Rights Data Analysis Group

Educational Background
Rice University: BA, Statistics and Mathematics (2006)
Duke University: PhD, Statistical Science (2010)

About Kristian
Kristian Lum is the lead statistician at the Human Rights Data Analysis Group (HRDAG). Previously, she worked as a data scientist at a small technology startup and was a research assistant professor in the Virginia Bioinformatics Institute at Virginia Tech.

Lum’s research primarily focuses on examining the uses of machine learning in the criminal justice system, developing 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. Although statistics and other quantitative disciplines are not the typical path to social impact, Lum is drawn to applications in which she can use her knowledge of statistics to address important societal problems and amplify the voices of marginalized groups and individuals.

In her work on statistical issues in criminal justice, Lum has studied uses of predictive policing—machine learning models to predict who will commit future crime or where it will occur. In her work, she has demonstrated that if the training data encodes historical patterns of racially disparate enforcement, predictions from software trained with this data will reinforce and—in some cases—amplify this bias. She also currently works on statistical issues related to criminal “risk assessment” models used to inform judicial decision-making. As part of this thread, she has developed statistical methods for removing sensitive information from training data, guaranteeing “fair” predictions with respect to sensitive variables such as race and gender. Lum is active in the fairness, accountability, and transparency (FAT) community and serves on the steering committee of FAT, a conference that brings together researchers and practitioners interested in fairness, accountability, and transparency in socio-technical systems.

Lum’s work on record linkage and capture-recapture focuses on solving problems that arise in HRDAG’s conflict casualty estimation work. In capture-recapture, she has proposed methods to obtain reliable casualty estimates, even when some victims are unlikely to be recorded. She is also the primary developer of the DGA package for R, which implements a popular Bayesian method for capture-recapture.

Lum is originally from Auburn, California—a small town in the foothills of the Sierra Nevada mountains. For as long as she can remember, she has enjoyed math and logic puzzles. Her interest in math took off in high school when she took calculus from an excellent professor at the local community college, Sierra College. For the first time, she felt she was learning why math worked, rather than how to manually implement an algorithm to solve an equation. In college, she took a few statistics courses as electives for her math degree and grew interested in discovering patterns in data that explain real-world phenomena. This led her to graduate school in statistics, where she first became involved with HRDAG after “cold emailing” the organization’s founder. She has been focused on applying statistics to important problems in human rights ever since.