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    Can ChatGPT outperform humans in faking a personality assessment while avoiding detection?

    Access Status
    In process
    Authors
    Robie, Chet
    Phillips, Jane
    Bourdage, Joshua S
    Christiansen, Neil D
    Dunlop, Patrick
    Risavy, Steven D
    Speer, Andrew
    Date
    2025
    Type
    Journal Article
    
    Metadata
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    Citation
    Robie, C. and Phillips, J. and Bourdage, J.S. and Christiansen, N.D. and Dunlop, P. and Risavy, S.D. and Speer, A. 2025. Can ChatGPT outperform humans in faking a personality assessment while avoiding detection? International Journal of Selection and Assessment. 33 (3): e70015.
    Source Title
    International Journal of Selection and Assessment
    DOI
    10.1111/ijsa.70015
    ISSN
    0965-075X
    Faculty
    Faculty of Business and Law
    School
    Future of Work Institute
    URI
    http://hdl.handle.net/20.500.11937/97883
    Collection
    • Curtin Research Publications
    Abstract

    Large language models (LLMs), such as ChatGPT, have reshaped opportunities and challenges across various fields, including human resources (HR). Concerns have arisen about the potential for personality assessment manipulation using LLMs, posing a risk to the validity of these tools. This threat is a reality: recent research suggests that many candidates are using AI to complete pre-hire assessments. This study addresses this problem by examining whether ChatGPT can outperform humans in faking personality assessments while avoiding detection. To explore this, two experiments were conducted focusing on assessing job-relevant traits, with and without coaching, and with two methods of identifying faking, specifically using an impression management (IM) measure and an overclaiming questionnaire (OCQ). For each study, we used responses from 100 working adults recruited via the Prolific platform, which were compared to 100 replications from ChatGPT. The results revealed that while ChatGPT showed some ability to manipulate assessments, without coaching it did not consistently outperform humans. Coaching had a minimal impact on reducing IM scores for either humans or ChatGPT, but reduced OCQ bias scores for ChatGPT. These findings highlight the limitations of current faking detection measures and emphasize the need for further research to refine methods for ensuring the integrity of personality assessments in HR, particularly as artificial intelligence becomes more available to candidates.

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