Video-Recordings of Eyewitness Identification in Actual Cases: The Postdictive Value of Eyewitness Behaviors

实际案件中目击者识别的录像：目击者行为的事后价值

• 批准号: 2017510
• 负责人: Andrew Smith
• 金额: $ 38.89万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2017510&HistoricalAwards=false
• 关键词: Video; Recordings; Eyewitness; Identification; Actual

Over 70% of DNA exonerations have been cases of mistaken eyewitness identification. Based on recommendations from eyewitness scientists, a large number of jurisdictions across the country have reformed their identification procedures to address this problem. Nevertheless, even the best lineup procedures fail to weed out all mistaken identifications. Hence, police and prosecutors still must attempt to sort between reliable identifications and unreliable identifications even when the best lineup procedures are followed. It is important to determine what variables can help police and prosecutors with this task. Postdiction variables are a particularly promising class of variables. Postdiction variables are variables that are influenced by the presence or absence of the guilty suspect in the lineup. These include eyewitness behaviors such as expressed level of confidence, the amount of time it takes the witness to make an identification decision, visible evidence of effort, verbal utterances, among others. The purpose of the present work is to examine how well postdiction variables extracted from video recordings of real-world witnesses making identification decisions can sort between reliable and unreliable identification decisions. To the extent that postdiction variables prove useful for sorting between reliable identifications and unreliable identifications, these findings would also encourage jurisdictions that are not yet video recording identification procedures to begin doing so. Indeed, only by video recording the entirety of the identification procedure can these jurisdictions ensure a complete and accurate record of these variables that can be used to sort between reliable and unreliable identifications.The goal of this research is to determine what combination of postdictors best sort between reliable eyewitness identifications and unreliable eyewitness identifications in real-world lineups. The findings will then be leveraged to develop an algorithm that police and prosecutors can use to assess the reliability of eyewitness identifications in future investigations. The District Attorney’s Office of Santa Clara County, CA and the San Jose Police Department will provide video-recordings of witnesses completing actual police lineups. Santa Clara County is unique as an early adopter of best-practice eyewitness identification procedures (a requirement for valid assessment of postdiction variables) and more recently implementing a policy of video-recording all lineups. As these videos become available, blind scorers (blind as to whether the identified person is the suspect or a filler) will assess a host of known postdiction variables. These postdiction scores will then be regressed on the outcome variable of whether the witness identified the suspect or a known-innocent filler. A critical mass of suspect identifications are likely to be culprit identifications whereas all identifications of fillers are definitive instances of mistaken identifications. Accordingly, postdiction variables that are useful for separating accurate from mistaken identifications should distinguish between suspect identifications and filler identifications. In addition to examining the predictive validity of known postdiction variables (e.g., confidence, decision time, verbal utterances), we will use an extensive coding scheme to code for numerous other eyewitness behaviors and we will examine whether these additional eyewitness behaviors can improve classification performance over and above the performance achieved with know postdiction variables.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.

超过70％的DNA免责是目击者识别错误的情况。根据目击者科学家的建议，全国各地的大量司法管辖区都改革了他们的身份证程序以解决这一问题。然而，即使是最好的阵容程序也无法清除所有错误的身份。因此，即使遵循最佳的阵容程序，警察和检察官仍必须尝试在可靠的识别和不可靠的身份方面进行分类。重要的是要确定哪些变量可以帮助警察和检察官完成这项任务。后变量是一个特别有希望的变量。后变量变量是由于阵容中有罪犯罪嫌疑人的存在或不存在的变量。这些包括目击者的行为，例如表达的信心水平，证人做出确定决定所花费的时间，可见的努力证据，口头话语等。本工作的目的是检查从现实世界证人的视频记录中提取的识别决定的识别和不可靠的识别决策之间的录像中提取的后变量的程度。在某种程度上，后期变量被证明可用于在可靠的识别和不可靠的识别之间进行分类，这些发现还将鼓励尚未视频记录识别程序开始这样做的管辖权。实际上，只有通过视频记录整个识别程序，这些管辖区才能确保这些变量的完整，准确的记录，这些变量可用于在可靠和不可靠的识别之间进行整理。这项研究的目的是确定后者最佳分类的最佳选择在可靠的目击者识别和不可靠的Eyewitness识别现实界限之间。然后，将利用这些发现来开发一种算法，检察官可以用来评估未来调查中目击者识别的可靠性。加利福尼亚州圣克拉拉县的地方检察官办公室和圣何塞警察局将提供完成实际警察阵容的证人的视频录制。圣克拉拉县（Santa Clara County）是最佳实践目击者识别程序的早期采用者（有效评估后数值变量的要求），并且最近实施了视频记录所有阵容的政策。随着这些视频的可用，盲目的得分手（对确定的人是犯罪嫌疑人还是填充者是盲目的）将评估许多已知的后期变量。然后，这些后分数得分将在证人确定犯罪嫌疑人还是已知的填充物的结果变量上进行回归。批判性可疑识别可能是罪魁祸首的识别，而所有填充物的标识都是确定性识别的确定实例。彼此之间，对将准确的识别与错误识别分开有用的后变量应区分可疑的识别和填充物识别。除了检查已知的后变量变量的预测有效性（例如，信心，决策时间，口头话语）外，我们还将使用广泛的编码方案为许多其他目击者的行为进行编码，我们还将使用这些额外的目击者行为来检查这些额外的目击者行为能够通过以上和上述绩效来提高分类绩效。基金会的智力优点和更广泛的影响评论标准。