AI通过面部表情和头部动作识别求职者说谎或伪装，准确率远超人类面试官。
Artificial Intelligence can Recognize Whether a Job Applicant is Selling and/or Lying According to Facial Expressions and Head Movements Much More Correctly Than Human Interviewers
- 用计算机视觉提取视频中面部与头部动作的时序模式。
- 模型对诚实与欺骗行为的预测方差解释率达91%和84%。
- 适合招聘筛选、行为分析及人机评估研究者参考。
求职者在视频面试中诚实与欺骗性表现是否可通过面部表情信号被识别，仍存争议且需深入研究。本研究利用深度学习与计算机视觉技术，从真实异步视频面试中提取求职者面部表情与头部动作的时序特征，以识别其自报的诚实与欺骗性印象管理（IM）策略。共收集了121名求职者的每段12至15分钟视频，每人回答5个结构化行为问题，并完成包含四个IM维度的信任度自评问卷。另通过现场实验，将模型预测结果与30名人类面试官对30段视频的评估进行对比。结果显示，模型对诚实与欺骗性IM的方差解释率分别为91%和84%，且与自评得分的相关性显著高于人类面试官。
原文摘要 · Abstract (English)
Whether an interviewee's honest and deceptive responses can be detected by facial expression signals in videos has been debated and requires further research. We developed deep learning models enabled by computer vision to extract temporal patterns of job applicants' facial expressions and head movements to identify self-reported honest and deceptive impression management (IM) tactics from video frames in real asynchronous video interviews. A 12- to 15-minute video was recorded for each of N=121 job applicants as they answered five structured behavioral interview questions. Each applicant completed a survey to self-evaluate their trustworthiness on four IM measures. Additionally, a field experiment was conducted to compare the concurrent validity associated with self-reported IMs between our modeling approach and human interviewers. Human interviewers' performance in predicting these IM measures from another subset of 30 videos was obtained by having N=30 human interviewers evaluate three recordings. Our models explained 91% and 84% of the variance in honest and deceptive IMs, respectively, and showed stronger correlations with self-reported IM scores than human interviewers.
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