Can Large Language Models Assess Personality From Asynchronous Video Interviews? A Comprehensive Evaluation of Validity, Reliability, Fairness, and Rating Patterns

• Published in: IEEE transactions on affective computing Vol. 15; no. 3; pp. 1769 - 1785
• Main Authors: Zhang, Tianyi, Koutsoumpis, Antonis, Oostrom, Janneke K., Holtrop, Djurre, Ghassemi, Sina, de Vries, Reinout E.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Affective computing; Algorithms; Analytical models; Artificial intelligence; Asynchronous video interviews; Bias; Computational modeling; Cost analysis; Human performance; Interviews; Large language models; Performance evaluation; Performance prediction; Personality; personality recognition; Personality traits; personnel selection; Psychology; Ratings; Reliability; Task analysis; Validity
• ISSN: 1949-3045; 1949-3045
• DOI: 10.1109/TAFFC.2024.3374875

The advent of Artificial Intelligence (AI) technologies has precipitated the rise of asynchronous video interviews (AVIs) as an alternative to conventional job interviews. These one-way video interviews are conducted online and can be analyzed using AI algorithms to automate and speed up the selection procedure. In particular, the swift advancement of Large Language Models (LLMs) has significantly decreased the cost and technical barrier to developing AI systems for automatic personality and interview performance evaluation. However, the generative and task-unspecific nature of LLMs might pose potential risks and biases when evaluating humans based on their AVI responses. In this study, we conducted a comprehensive evaluation of the validity, reliability, fairness, and rating patterns of two widely-used LLMs, GPT-3.5 and GPT-4, in assessing personality and interview performance from an AVI. We compared the personality and interview performance ratings of the LLMs with the ratings from a task-specific AI model and human annotators using simulated AVI responses of 685 participants. The results show that LLMs can achieve similar or even better zero-shot validity compared with the task-specific AI model when predicting personality traits. The verbal explanations for predicting personality traits generated by LLMs are interpretable by the personality items that are designed according to psychological theories. However, LLMs also suffered from uneven performance across different traits, insufficient test-retest reliability, and the emergence of certain biases. Thus, it is necessary to exercise caution when applying LLMs for human-related application scenarios, especially for significant decisions such as employment.