Essays on Criminal Justice and Inequality

This dissertation encompasses three essays on policing and criminal justice, algorithms and inequality. The first two essays examine the efficacy and equity implications of data-driven algorithms that are increasingly used in important life-altering decision-making contexts. The third essay investigates when crime responds to punishment.

The first essay studies the impacts of neighborhood targeting of police presence brought about by predictive policing algorithms on crime and arrests. While predictive policing is widely used, the impacts of neighborhood targeting brought about by predictive policing on crime, and whether there are disproportionate racial impacts are open questions. Using a novel dataset, I isolate quasi-experimental variation in police presence induced by predictive-policing algorithms to estimate the causal impacts of algorithm-induced police presence. I find that algorithm-induced police presence decreases serious violent and property crime, and evidence that algorithm-induced neighborhood targeting of police presence has disproportionate racial impacts on traffic incident arrests and serious violent crime incident arrests.

The second essay investigates how data-driven algorithms can maximize overall predictive power at the cost of racial and economic justice. Examining a tool that is already widely used in pretrial decision-making, I build a framework to evaluate how input variables trades off overall predictive power, and racial and economic disparities in the scores that defendants receive. I find that using information on neighborhoods where defendants live only marginally contributes to overall predictive power. However, the use of defendant neighborhood data substantially increases racial and economic disparities, suggesting that machine learning objectives tuned to maximize overall predictive power risk being in conflict with racial and economic justice.

Finally, in the third essay, joint with Sarah Komisarow and Robert Gonzalez, we examine when crime responds to punishment severity increases. While economic theory suggests that crime should respond to punishment severity, empirical evidence on this link is ambiguous. We propose an explanation for this empirical evidence -- the effect of punishment severity increases depends on the probability of detection; punishments deter crime when the probability of detection is moderate. We test and validate this explanation using increases in punishment severity in drug-free school zones along with changes in the probability of detection resulting from a community crime-monitoring program.