Chet Robie, Jane Phillips, Joshua S. Bourdage, Neil D. Christiansen, Patrick D. Dunlop, Stephen D. Risavy, Andrew B. Speer
{"title":"ChatGPT能在不被发现的情况下伪造人格评估,胜过人类吗?","authors":"Chet Robie, Jane Phillips, Joshua S. Bourdage, Neil D. Christiansen, Patrick D. Dunlop, Stephen D. Risavy, Andrew B. Speer","doi":"10.1111/ijsa.70015","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":51465,"journal":{"name":"International Journal of Selection and Assessment","volume":"33 3","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/ijsa.70015","citationCount":"0","resultStr":"{\"title\":\"Can ChatGPT Outperform Humans in Faking a Personality Assessment While Avoiding Detection?\",\"authors\":\"Chet Robie, Jane Phillips, Joshua S. Bourdage, Neil D. Christiansen, Patrick D. Dunlop, Stephen D. Risavy, Andrew B. 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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.</p>\",\"PeriodicalId\":51465,\"journal\":{\"name\":\"International Journal of Selection and Assessment\",\"volume\":\"33 3\",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2025-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/ijsa.70015\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Selection and Assessment\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/ijsa.70015\",\"RegionNum\":4,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Selection and Assessment","FirstCategoryId":"91","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/ijsa.70015","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MANAGEMENT","Score":null,"Total":0}
Can ChatGPT Outperform Humans in Faking a Personality Assessment While Avoiding Detection?
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.
期刊介绍:
The International Journal of Selection and Assessment publishes original articles related to all aspects of personnel selection, staffing, and assessment in organizations. Using an effective combination of academic research with professional-led best practice, IJSA aims to develop new knowledge and understanding in these important areas of work psychology and contemporary workforce management.