SCC-CIVIC-PG Track B: Everyday Respect: Measuring & Improving Police Officer Communication During Motor Vehicle Stops

警察实施的交通拦截既平凡又可能致命。该项目将使用佩戴式摄像机的音频和视频片段来研究洛杉矶警察局（LAPD）官员和司机在这些停车场之间的沟通。我们将研究官员在停留初期的沟通如何影响互动的展开，包括互动是否升级。我们还将研究种族，性别，阶级或其他差异是否存在于社区成员在停止期间如何对待，以及种族，残疾状况，社区背景和军官培训等因素如何相互作用，以塑造军官的沟通方式。该项目的第二阶段还包括与洛杉矶警察学院合作，根据我们的调查结果制定和实施新的培训课程，并与洛杉矶警察局宪法警务和政策办公室合作，探讨对部门政策的可能修订。这项以社区为主导的研究利用了身体佩戴和仪表盘摄像头的镜头，以及补充的停止数据，人员数据和基于位置的社区背景数据。我们超越了这一领域的现有工作，根据我们的沟通措施，从洛杉矶警察局和广泛的社区成员和社区组织征求意见，就应衡量的“良好”沟通的各个方面（第一阶段）。我们将雇用不同的人类注释者群体来编码我们确定为对社区利益相关者显着的官员沟通的所有维度（第2阶段）。我们将使用机器学习工具，对这些人类注释进行训练，然后测量大规模的沟通，然后对官员沟通的原因和后果进行统计分析，包括可能导致社区中某些群体受到不同对待的因素。该项目是对公民创新挑战计划-轨道B的回应。弥合基本资源和服务社区需求之间的差距-是NSF、国土安全部和能源部之间的合作。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Traffic stops conducted by police officers are both mundane and potentially deadly. This project will use audio and video footage from body-worn cameras to study the communication between Los Angeles Police Department (LAPD) officers and drivers during these stops. We will examine how officers’ communication early on in a stop shapes how the interaction unfolds, including whether or not it escalates. We will also examine whether race, gender, class or other disparities exist in how community members are treated during stops, as well as how factors like race, disability status, community context, and officer training interact to shape how officers communicate. Stage 2 of this project also includes collaboration with the Los Angeles Police Academy to develop and implement new training curricula drawing on our findings and collaboration with the LAPD Office of Constitutional Policing and Policy to explore possible revisions to department policy. This community-led study draws on footage from body-worn and dashboard cameras, as well as complementary stop data, personnel data, and location-based data on community context. We move beyond existing work in this area by basing our measures of communication on views solicited from both the LAPD and a wide range of community members and community organizations regarding the various aspects of “good” communication that should be measured (Stage 1). We will employ a diverse population of human annotators to code all the dimensions of officer communication we identify as salient to community stakeholders (Stage 2). We will use machine learning tools, trained on these human annotations, to then measure communication at scale and then proceed to statistical analysis of both the causes and consequences of officer communication, including the factors that may lead some groups in the community to be treated differently than others.This project is in response to the Civic Innovation Challenge program—Track B. Bridging the gap between essential resources and services & community needs—and is a collaboration between NSF, the Department of Homeland Security, and the Department of Energy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.