自动化视频采访是否足够聪明? 机器学习的行为模式,可靠性,有效性和偏见认知能力评估
Louis Hickman1, Louis Tay2, Sang Eun Woo2
1Department of Psychology, Virginia Tech.
The Journal of applied psychology
|September 26, 2024
概括
使用机器学习 (ML) 的自动视频采访 (AVI) 可以评估认知能力. 在AVI中,口头行为是关键的预测因素,但需要进一步的研究来提高这些AI驱动的评估的区分有效性.
科学领域:
- 心理学 心理学 心理学
- 人工智能的人工智能
- 人力资源 人力资源 人力资源
背景情况:
- 利用机器学习 (ML) 的自动视频采访 (AVI) 越来越多地用于候选人评估.
- 之前的研究主要集中在非认知特征上,通过AVI对认知能力的评估未得到充分研究.
研究的目的:
- 批判性地评估AVI在评估认知能力方面的潜力.
- 开发和检查ML模型来预测一般的心理能力,语言能力和智力.
- 为了更好地理解认知能力和受访者的行为之间的关系.
主要方法:
- 开发和分析AVI ML模型,训练它预测三个认知能力结构.
- 利用两个模拟视频采访样本来评估心理特征.
- 评估了AVI分数的可靠性,有效性 (融合,标准,歧视) 和公平性/偏见.
主要成果:
- 在AVI中,口头行为是认知能力的最强的预测因素; 准语言和非语言行为缺乏增量有效性.
- AVI ML模型捕获了一般心理能力,语言能力和智力的行为表现.
- 有效性证据是混合的;受观察者评级智力训练的模型显示出强烈的收和标准有效性,但有限的判别有效性.
结论:
- 语言行为对于评估AVI的认知能力至关重要.
- 检查超出简单的趋同的心理特征对于验证基于ML的评估至关重要.
- 提供了建议,以改善未来AVI研发中的差别有效性.
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