Using AI to improve our understanding of verbal confidence and to aid decision-making: Eyewitness lineup identification as a model case

使用人工智能提高我们对言语信心的理解并辅助决策：以目击者阵容识别为典型案例

• 批准号: 2241989
• 负责人: Chad Dodson
• 金额: $ 36.7万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2241989&HistoricalAwards=false
• 关键词: Using; AI; improve; our; understanding

From medical treatment to predicting recidivism, artificial intelligence (AI) is playing an increasingly larger role in human decision-making – in large part because AI predictions are superior to human predictions in a variety of different domains. This project uses eyewitness lineup identifications as a model paradigm. One aim of the project uses AI to better understand verbal expressions of confidence about an identification. For example, when an eyewitness states “I’m pretty sure it’s him” about a lineup identification what is the likelihood that the eyewitness’s identification is correct? Another aim of the project examines how best to convey AI output to people so as to improve their predictions about the accuracy of an eyewitness’s identification. The project consists of two sets of experiments. One set uses variations on an eyewitness memory paradigm to examine the predictive value of verbal (e.g., “I’m pretty certain”) and numeric (e.g., “I’m 75% certain”) expressions of confidence. It is largely assumed that verbal confidence statements reflect the same underlying information as numeric confidence ratings. Machine-learning classifiers are used to quantify verbal confidence to explain why verbal confidence is not redundant with numeric confidence but can contribute unique added value in predicting the accuracy of a response. A second set of experiments test two predictions. First, that machine learning estimates about the accuracy of a lineup identification are more resistant than human estimates to the effects of various kinds of contextual information. Second, that a cognitive-forcing method of conveying machine-learning estimates about eyewitness identification accuracy is most effective at improving people’s predictions about eyewitness accuracy, particularly under conditions when people’s intuitions about eyewitness performance are wrong. Overall, this project addresses a need to identify issues in human-algorithm interactions before the spread of AI-assistance to the domain of eyewitness identification.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.

从医疗到预测再犯罪，人工智能(AI)在人类决策中发挥着越来越大的作用-很大程度上是因为AI预测在各种不同领域优于人类预测。该项目使用目击者名单识别作为模型范例。该项目的一个目标是使用人工智能来更好地理解关于身份识别的自信的口头表达。例如，当一名目击者说“我很确定就是他”时，目击者的身份证明是正确的可能性有多大？该项目的另一个目标是研究如何最好地将人工智能的输出传达给人们，以提高他们对目击者身份识别的准确性的预测。该项目由两组实验组成。其中一组使用目击者记忆范式的变化来检验自信的口头表达(例如，“我很确定”)和数字(例如，“我有75%确定”)的预测值。在很大程度上，人们认为言语自信陈述反映了与数字置信度评级相同的基本信息。机器学习分类器被用来量化言语自信，以解释为什么言语自信与数字自信不是多余的，而是可以在预测反应的准确性方面贡献独特的附加值。第二组实验验证了两个预测。首先，机器学习对阵容识别准确性的估计比人类对各种上下文信息的影响更具抵抗力。第二，传递目击者识别准确性机器学习估计的认知强迫方法在改善人们对目击者准确性的预测方面最有效，特别是在人们对目击者表现的直觉错误的情况下。总体而言，该项目解决了在人工智能辅助扩展到目击者识别领域之前识别人-算法交互中的问题的需要。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。