使用基于大型语言模型的人工智能 (AI) 的嫌疑人训练战略使用证据:转移到模拟嫌疑人采访的初步证据
Siyu Li1, Pär-Anders Granhag2, Yunhan Shi1
1Faculty of Arts and Sciences, New York University Shanghai.
Law and human behavior
|January 20, 2026
概括
使用人工智能 (AI) 模拟采访的嫌疑人提高了谎言检测技能. 与AI嫌疑人的训练提高了参与者在现实世界互动中识别欺骗的能力,尽管与单独的指令相比,效果很小.
科学领域:
- 认知心理学 认知心理学
- 在行为科学中的人工智能
- 欺骗 检测 调查 研究 发现 欺骗
背景情况:
- 战略性使用证据 (SUE) 模型训练面试官区分真实者和骗子.
- 传统的SUE培训缺乏实际应用的机会,限制了技能转移.
- 大型语言模型 (LLM) 驱动的人工智能 (AI) 为真实的面试模拟提供了潜力.
研究的目的:
- 调查人工智能驱动的SUE模拟在提高欺骗检测技能的有效性.
- 确定与人工智能嫌疑人的培训是否可以改善将SUE技术转移到与人类嫌疑人的互动.
- 为了比较人工智能辅助培训与传统的仅教学方法的有效性.
主要方法:
- 156名中国非专业人士作为采访者和人类模拟嫌疑人参与其中.
- 人工智能嫌疑人被编程成与谎言者和说实话者的SUE模型相一致的行为.
- 面试人员接受了三个培训条件之一:指导和AI练习,仅指导或控制.
- 在培训后,参与者采访了撒谎或真实的人类模拟嫌疑人.
主要成果:
- 干预小组 (指导和AI练习,仅指导) 更频繁地使用证据框架策略.
- 与对照组相比,这两组干预组在可疑陈述中引起了更多的不一致性.
- 与对照组相比,指令和人工智能练习组在判断欺骗方面表现出更高的准确性.
结论:
- 与AI嫌疑人的模拟SUE培训显示了可转移到人类互动的初步证据.
- 人工智能辅助培训在欺骗检测准确度方面比单独的指令提供了轻微的优势.
- 人工智能模拟对传统指令的好处并不显著,这表明需要进一步改进.
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