Kurschilgen, Michael Kecht, Christoph Strobel, Magnus 2022 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. application/pdf https://n2t.net/ark:/51647/srd1323044 https://sonar.unidistance.ch/global/documents/323044 https://sonar.unidistance.ch/documents/323044/files/revival-cover-letter-220905.pdf eng info:eu-repo/semantics/altIdentifier/ark/51647/srd1323044 info:eu-repo/semantics/openAccess License undefined ICIS 2022 Proceedings. - 2022 Personality Assessment, Cooperativeness, Big Five, Linguistic Inquiry and Word Count, Machine Learning, Natural Language Processing info:eu-repo/classification/udc/33 Revival of the Cover Letter? Experimental Evidence on the Performance of AI-driven Personality Assessments http://purl.org/coar/resource_type/c_5794