at Google ScholarTitle data
Kecht, Christoph ; Kurschilgen, Michael ; Strobel, Magnus:
Revival of the Cover Letter? Experimental Evidence on the Performance of AI-Driven Personality Assessments.
In:
Proceedings of the 43rd International Conference on Information Systems (ICIS). -
Copenhagen, Denmark
,
2022
Abstract in another language
Organizations have long been trying to assess job applicants' personality using self-reported psychometric tests, such as the Big Five personality test. However, these tests are not robust against incentives to pretend having certain desirable traits, for example, the disposition for being a good team player. We test whether machine learning classifiers trained on written self-descriptions, such as cover letters, predict people's true cooperativeness better than psychometric tests. Based on data from a controlled online experiment with 400 participants, we find that - when people have incentives to fake their personality - linguistic classifiers based on self-descriptions significantly outperform psychometric classifiers based on the Big Five. Moreover, we find that a fine-tuned, pre-trained natural language model can detect incentives to fake in people's self-descriptions. While further research is needed to achieve tamper-proof models, our findings illustrate the potential of automated personality tests based on job applicants' cover letters.