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

执法人员进行的交通拦截包括常规遭遇以及可能造成死亡的情况。该项目旨在通过随身携带的摄像机的音频和视频片段来研究交通停站期间的通信动态。这项研究建立在证据的基础上，即警官最初的沟通会影响互动的过程，以及是否会升级。此外，该项目将根据人口因素和其他相关变量审查社区成员待遇方面的潜在差异。该项目还将研究人口特征、残疾状况、社区背景和警官培训等因素之间的相互作用，以形成警官和司机之间的互动。与培训学院合作是根据研究成果制定和实施新的培训课程的必要条件。本研究采用社区知情的方法，结合了随身携带的摄像头的镜头，以及关于站点、人员、司机行为和社区背景的补充数据。该项目包括所研究的部门和其他社区利益相关者的意见，以确定应评估的有效沟通的维度。来自不同背景的人类注释者被用来编码军官交流的识别维度。经过这些人工注释训练的机器学习工具，使项目团队能够大规模地分析通信模式。项目小组进行统计分析，调查人员沟通的原因和后果，包括可能导致社区内不同群体受到差别待遇的因素，并开发可在培训学院测试的新培训工具。公民创新挑战赛是能源部、国土安全部和国家科学基金会的合作项目。该奖项反映了美国国家科学基金会的法定使命，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 carried out by law enforcement officers include routine encounters as well as situations that hold the potential for fatalities. This project aims to investigate the communication dynamics during traffic stops conducted using audio and video footage from body-worn cameras. The research builds on evidence that officers' initial communication influences the course of the interaction and whether it escalates or not. Additionally, the project will examine potential disparities in the treatment of community members based on demographic factors and other relevant variables. The project will also study the interplay among factors such as demographic characteristics, disability status, community context, and officer training in shaping officer and driver interactions. Collaboration with a training academy is integral to developing and implementing new training curricula based on the research findings. This study takes a community-informed approach by incorporating footage from body-worn cameras, as well as complementary data on stops, personnel, driver behavior, and community context. The project includes input from the department studied and other community stakeholders to define the dimensions of effective communication that should be evaluated. Human annotators from diverse backgrounds are employed to code the identified dimensions of officer communication. Machine learning tools, trained on these human annotations, enable the project team to analyze communication patterns at scale. The project team conducts statistical analyses to investigate the causes and consequences of officer communication, including factors that may contribute to differential treatment of different groups within the community, and to develop new training tools that can be tested at the training academy.The CIVIC Innovation Challenge is a collaboration with Department of Energy, Department of Homeland Security, and the National Science Foundation.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.