IBSS: Measuring Justice, Managing Equity: An Interdisciplinary, Multi-Level Approach to Understanding Bias and Equity in Police Stops and Use of Force

这个项目关注的是什么导致了警察中的种族差异，以及如何使用社会学、人口学、公共政策、犯罪学、心理学和行为经济学中的工具来衡量这些差异。对于跨学科的研究人员来说，理解当代种族不平等的原因尤其令人烦恼，因为美国种族偏见的稳步下降并没有伴随着社会机构(例如教育、医疗、就业、刑事司法)中种族不平等的类似下降。这意味着，关于种族不平等是如何产生的传统理论不足以解释当代的种族动态，需要新的研究模型来产生对社会不平等的新理解。这个项目利用警察领导公平联盟和美国各地执法部门之间的现有研究合作，产生一种混合方法来理解复杂系统(即警务)中造成种族不平等的原因。调查人员将与大约25个执法伙伴合作，首先对警察拦截行人和驾车者以及警察使用武力的数据收集进行标准化。其次，研究人员将收集官员的心理调查，汇总部门内各区的回应。最后，研究人员将使用社会空间数据、部门政策数据、汇总的官员态度数据以及“坏苹果”对整个部门不平等比率的相对贡献的数据，解决执法中关于种族的一些基本问题，以及当地人口统计和整个部门的文化在造成种族不平等方面所起的作用。跨学科研究小组和大量参与的警察部门为不同类型部门的多层次分析和比较提供了便利，产生了结果不平等的症状。虽然之前的研究着眼于警察与公民的遭遇(拦截、搜查、逮捕、武力等)。或有节制的偏见，它很少同时做到这两点。因此，这个项目将有助于科学地理解是什么导致了警察中的种族不平等，并确定了从业者可以衡量和管理这种不平等的方法。总之，调查人员打算促进开发和验证警察拦截和使用武力数据收集、分析和解释的国家标准方法。调查结果将广泛传播给警察部门，直接为政策提供信息，并为减少警察成果中种族不平等的国家最佳做法奠定科学基础。该项目还将为博士后研究员和研究生提供一个独特的机会，在应用和多学科的背景下审查种族偏见，同时使他们接触到更复杂和多样化的数据分析战略。该项目通过NSF跨学科行为和社会科学研究(IBSS)竞赛得到支持。

This project focuses on what causes racial disparities in policing and how one can measure these disparities using tools taken from sociology, demography, public policy, criminology, psychology, and behavioral economics. Understanding what causes contemporary racial inequality is particularly vexing for researchers across disciplines because the US's steady decline in racial prejudice has not been accompanied by an analogous decline in racial inequality across social institutions (e.g., education, healthcare, employment, criminal justice). This has meant that traditional theories of how racial inequality is produced are insufficient to explain contemporary racial dynamics, requiring new research models to produce new understandings of social inequality. This project capitalizes on existing research collaborations between the Consortium for Police Leadership in Equity and law enforcement departments across the US to produce a mixed-methods approach to understanding what produces racial inequality in complex systems (i.e., policing). In cooperation with approximately 25 law enforcement partners, the investigators will first standardize data collection regarding police stops of pedestrians and motorists as well as police use of force. Second, researchers will collect psychological surveys from officers, aggregating responses by districts within departments. Finally, using social-spatial data, department policy data, aggregate officer attitude data, and data about the relative contributions of "bad apples" to department-wide disparity rates, the researchers will address some of the fundamental questions about race in law enforcement as well as the role that local demographics and department-wide culture have in producing racial inequality. The interdisciplinary research team and large number of participating police departments facilitate inquiry across multiple levels of analysis and comparisons across different types of departments, producing a symptomology of unequal outcomes. While previous research has looked at police-citizen encounters (stops, searches, arrests, force, etc.) or measured biases, it has rarely done both together. Consequently, this project will contribute both to scientific understandings of what causes racial inequality in policing and identifying ways practitioners can measure and manage that inequality.Taken together, the investigators intend this project to promote the development and validation of a nationally standardized approach to police stops and use of force data collection, analysis, and interpretation. The findings will be widely disseminated to police departments, directly informing policy and setting the scientific foundations for national best practices to reduce racial inequality in police outcomes. This project will also provide postdoctoral fellows and graduate students a unique opportunity to examine racial bias in an applied and multidisciplinary context, while exposing them to more complex and diverse data analytic strategies. This project is supported through the NSF Interdisciplinary Behavioral and Social Sciences Research (IBSS) competition.