Description of original award (Fiscal Year 2017, $452,553)
The researchers propose to use machine learning (ML) techniques to develop a tool that predicts risk of domestic violence (DV) victimization and to work with NYPD to test the efficacy of better targeting of high risk IPV victims through a large-scale randomized controlled trial.
The ML tool will combine administrative NYPD data with data from sources (e.g., 911 calls). The team will also use techniques to extract signals from text in officer reports, victim statements, and 911 calls.
Preliminary results suggest that an early version of the algorithm considerably outperforms both existing risk tools and business-as-usual decision-making by police officers. In partnership with NYPD, the team will formally test this via RCT of the tool to compare the effects of targeting officer home visits to victimsan intervention that has been found promising in quasi-experimental evaluationsusing ML relative to status quo.
Key outcomes include rates of repeat victimization and serious injury, as measured by NYPD crime complaints and 911 calls (to capture calls for ambulance service, even if police are not called).
Note: This project contains a research and/or development component, as defined in applicable law,and complies with Part 200 Uniform Requirements - 2CFR 200.210(a)(14).
- Examining the Black Box: A Formative and Evaluability Assessment of Cross-sectoral Approaches for Intimate Partner and Sexual Violence
- Access to Justice for Adolescents and Young Adults Experiencing Intimate Partner Violence: Effectiveness and Accessibility of Civil Protection Orders
- Preventing the next sext: A behavioral economic approach to understanding nude photo sharing decisions in a high school community