Modeling Evaluation Methods for Eyewitness Identification

在刑事司法系统中，目击证人鉴定在司法工作中起着重要作用。然而，目击者犯下的错误可能导致严重的法律后果，无辜的嫌疑人被错误定罪，有罪的嫌疑人逃脱惩罚并继续危害社会。由于目击证人证词的复杂性和相关的法律后果，制定评估目击证人证词的有效方法至关重要。该项目将采用建模方法来开发目击者识别的有效评估方法。采用数学建模方法，该项目将推进并应用预期成本模型，以整合警方在进行目击证人身份识别之前、期间和之后的调查实践。通过整合元分析数据和数学模型，该项目的预期成本模型将为严格分析目击者的表现提供一个综合框架，并在广泛的情况下做出有用的预测。本研究将分析目击者研究中常用的评估方法的含义，以便在分析目击者数据和评估鉴定程序方面建立科学共识。该项目还将开发计算和可视化工具，以提高预期成本模型和其他评估方法的正确使用。研究结果将提高对目击证人表现的理解，为最佳法律实践提供科学证据，并促进在目击证人研究中科学地使用数学建模方法。所有这些成果对于发展对目击者鉴定的科学理解和改进鉴定实践以促进正义至关重要。该项目由社会、行为和经济科学理事会（SBE）的法律和科学计划以及促进竞争研究的既定计划（EPSCoR）共同资助。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。

Eyewitness identification plays an important role in administering justice in the criminal justice system. Yet, eyewitnesses make mistakes that can lead to grave legal consequences, with innocent suspects who are wrongfully convicted and guilty suspects who escape punishment and continue to harm society. Because of the complexity of eyewitness responses and associated legal consequences, it is critical to develop effective methods for evaluating eyewitness performance. This project will incorporate a modeling approach to develop effective evaluation methods for eyewitness identification.Employing a mathematical modeling approach, this project will advance and apply an expected cost model to integrate police investigation practices before, during, and after conducting an eyewitness identification. By incorporating meta-analytical data and mathematical modeling, the project’s expected cost model will provide an integrative framework for rigorously analyzing eyewitness performance and for making useful predictions under a broad range of circumstances. This research will analyze the implications of the evaluation methods commonly used in eyewitness research in order to build toward scientific consensus for analyzing eyewitness data and assessing identification procedures. This project will also develop computational and visualization tools to enhance proper use of the expected cost model and other evaluation methods. Findings will improve understanding of eyewitness performance, produce scientific evidence for optimal legal practices, and promote the scientific use of mathematical modeling approaches in eyewitness research. All of these outcomes are crucial in developing scientific understanding of eyewitness identification and improving identification practices to advance justice.This project is jointly funded by the Law and Science Program in the Directorate for Social, Behavioral & Economic Sciences (SBE) and the Established Program to Stimulate Competitive Research (EPSCoR).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.