Session 2: Leveraging Technology to Improve Police-Community Relations

Quick Overview

The research team leveraged large language models (LLMs) and a custom data infrastructure built over three years from 1.3 million body-worn camera videos across two police departments to analyze racial disparities in officer-driver interactions, finding that officers use less respectful language and are more likely to issue orders to Black drivers compared to White drivers, even before the drivers speak, but that the data-driven approach can also be used to evaluate and potentially improve police training and community relations.

Key Points: The project analyzed 1.3 million pieces of body-worn camera footage over three years from two Bay Area police departments involving 2,600 officers. The analysis revealed significant racial disparities: officers spoke less respectfully and were more likely to issue orders to Black drivers than White drivers. Black drivers were found to be handcuffed, searched, and arrested at higher rates across interactions in historically redlined areas (Areas 1 & 5) compared to non-redlined areas (Areas 2, 3, & 4). LLMs achieved 71% accuracy in predicting if a stop would escalate based only on the officer's first words. The research team developed a data infrastructure allowing for real-time analysis of officer-public interactions, which is crucial for evaluating training effectiveness. Explicit data analysis showed that Black drivers received fewer justifications for stops (e.g., 'Running a light is dangerous') and more direct orders compared to White drivers.

Context: This presentation details a research project titled "Leveraging Technology to Improve Police-Community Relations," led by Jennifer Eberhardt (Main PI) and Co-PIs Dan Jurafsky and Benoît Monin, along with Ralph Banks. The core of the work involved analyzing massive amounts of body-worn camera footage to quantitatively study racial disparities in police-community interactions, specifically focusing on the language used by officers during traffic stops. The project highlights the potential of AI and large language models to analyze complex human interactions at scale to promote accountability and systemic change.