Public Safety Lab

With the support of the Global Institute of Advanced Study, the Public Safety Lab has been launched to apply the tools of data science and social science to the project of producing better public safety outcomes.

For example, many relatively minor offenses are easy to detect and interdict. Many more serious offenses are much harder to detect and interdict. As a consequence we may overpunish relatively minor offenses, and underpunish more serious offenses. The Public Safety Lab works with jurisdictions to try to identify and minimize both overpunishment and underpunishment.

As an example of a project involving possible overpunishment, we are working with a large urban district attorney’s office to investigate the impact on offender recidivism of recent reforms designed to reduce the incidence of arrest and prosecution for relatively minor offenses. In many jurisdictions, offenders who commit relatively minor violations are arrested and prosecuted to the full extent of the criminal law. In these jurisdictions, cases involving relatively minor offenses typically consume a large share of law enforcement, prosecutorial, and judicial resources. Yet it is unclear whether this practice reduces offender recidivism; the practice may actually result in increased recidivism, and/or the escalation of minor offending into more serious offending. This project will allow us to report evidence of the impact on offender recidivism of reducing punishments for petty offenses. We also are pursuing a related project on the impact of the length of pretrial detention in county jails on offender recidivism.

As an example of a project involving possible underpunishment, we are working with a federal law enforcement agency to identify likely victims of sex trafficking in online prostitution markets. Sex trafficking is a crime of extraordinary violence visited largely upon vulnerable girls and young women. Its frequency is likely severely underreported, given the isolation within which its victims are kept. We are developing and implementing a sex trafficking analytic that predicts instances of trafficking from the corpus of online commercial sex ads and provider reviews, using an extensive set of verified instances of trafficking sourced from multiple law enforcement agencies. The analytic has the capacity to push predicted risk scores to agencies for investigation through a searchable database of ads and reviews; validated outcomes can be used to further refine the prediction model. We are also developing an analogous analytic to identify likely instances of illegal firearms trafficking in online firearms markets.

Descriptions of our other projects may be found on the lab’s website,

Project members:

Principal Investigator: Anna Harvey (NYU)

Mike Cafarella (University of Michigan)

Chris Dawes (NYU)

Greg DeAngelo (Claremont Graduate University)

Sharad Goel (Stanford)

Sanford Gordon (NYU)

Wei Long (Tulane)

Pam Metzger (Southern Methodist University)

Murat Mungan (George Mason University)

Daniel Neill (Carnegie Mellon University)

Ravi Shroff (NYU)

Hye Young You (NYU)