Session 2: Leveraging Technology to Improve Police-Community Relations

Quick Overview

The research team successfully leveraged technology, including Large Language Models (LLMs) and human annotators, to analyze 1.3 million pieces of body-worn camera footage from two Bay Area police departments over three years, revealing systemic racial disparities in officer language, such as officers being significantly more likely to issue orders and less likely to state the reason for a stop to Black drivers compared to White drivers.

Key Points: The project analyzed 1.3 million pieces of body-worn camera footage from two Bay Area police departments over three years, involving 2,600 officers and 200k administrative records. LLMs achieved 71% accuracy in predicting whether a stop would escalate based only on the officer's first words. Officers were significantly less likely to state the reason for the stop and more likely to issue an order in their first 45 words when addressing Black drivers compared to White drivers. Black drivers received measurably less respectful language (a negative score of -0.1) compared to White drivers in the high-redlined Area 1 & 5, while stops in lower-redlined areas (2, 3, & 4) showed less disparity (-0.06). Survey results indicated Black Americans (34.94%) rated their treatment as very bad/very well compared to White Americans (51.64%) in high-redlined areas. The team developed new tools to analyze longitudinal data, allowing for the evaluation of training effectiveness on officer behavior in real-world interactions, which was previously impossible at scale. The research concluded that police-community relations are impacted by systemic issues, and technology can be used to foster trust and accountability by identifying these patterns.

Context: This presentation, titled "Leveraging Technology to Improve Police-Community Relations," was delivered by a multidisciplinary team including Jennifer Eberhardt (Social Psychologist), Dan Jurafsky (Linguist/Computer Scientist), Ralph Banks (Law Professor), and Benoît Monin (Psychology Professor). The project focused on using AI and human annotation to systematically analyze thousands of police-citizen interactions captured on body-worn cameras, aiming to uncover subtle linguistic and behavioral patterns that contribute to disparities in police-community relations, particularly in the context of historical housing segregation (redlining).