Big five personality traits prediction with AI
Introduction Openness, conscientiousness, extroversion, agreeableness and neuroticism are known as the Big Five personality traits (BFPT). They are theoretical building blocks of the personality and comprise wide and interconnected spectra. Artificial intelligence (AI) could help to grasp their co...
| Published in: | European Psychiatry |
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| Main Author: | |
| Format: | Article |
| Language: | English |
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Cambridge University Press
2021-04-01
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| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S0924933821011895/type/journal_article |
| _version_ | 1850418928127311872 |
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| author | A. Mereu |
| author_facet | A. Mereu |
| author_sort | A. Mereu |
| collection | DOAJ |
| container_title | European Psychiatry |
| description |
Introduction
Openness, conscientiousness, extroversion, agreeableness and neuroticism are known as the Big Five personality traits (BFPT). They are theoretical building blocks of the personality and comprise wide and interconnected spectra. Artificial intelligence (AI) could help to grasp their complexity.
Objectives
To investigate whether AI could predict the BFPT from themselves.
Methods
Data from 2,697 questionnaires were analysed using an AI. The short form of the International Personality Item Pool was used to assess the BFPT. Four of the BFPT scores were employed to predict the fifth one and the procedure was repeated for all of them alternatively. The AI was conservatively tuned to maximize the one-way random intraclass correlation coefficient (ICC) between predicted and real values. Their Pearson’s r was calculated too. The free and open source programming language R was used for all the analyses. Dataset source: Hansson, Isabelle; Berg, Anne Ingeborg; Thorvaldsson, Valgeir (2018), “Can personality predict longitudinal study attrition? Evidence from a population-based sample of older adults”, Mendeley Data, V1, doi: 10.17632/g3jx8zt2t9.1
Results
Openness, conscientiousness, extroversion, agreeableness and neuroticism predictions obtained ICC of 0.219, 0.146, 0.306, 0.354, 0.121 and Pearson’s r of 0.254, 0.149, 0.393, 0.446, 0.122 respectively. The results for extroversion and agreeableness were indicative of fair performance.
Conclusions
AI might be useful to predict personality traits, mainly extroversion and agreeableness. This could be utile in many situations, such as dealing with missing data or deciding whether to formally test someone. Finally, the AI used in this study is freely available, allowing anyone to experiment.
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| format | Article |
| id | doaj-art-e8e7de35faa44d469ac4d7b73eaeccdb |
| institution | Directory of Open Access Journals |
| issn | 0924-9338 1778-3585 |
| language | English |
| publishDate | 2021-04-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| spelling | doaj-art-e8e7de35faa44d469ac4d7b73eaeccdb2025-08-19T22:43:43ZengCambridge University PressEuropean Psychiatry0924-93381778-35852021-04-0164S445S44610.1192/j.eurpsy.2021.1189Big five personality traits prediction with AIA. Mereu0Research performed independently, Cagliari, Italy Introduction Openness, conscientiousness, extroversion, agreeableness and neuroticism are known as the Big Five personality traits (BFPT). They are theoretical building blocks of the personality and comprise wide and interconnected spectra. Artificial intelligence (AI) could help to grasp their complexity. Objectives To investigate whether AI could predict the BFPT from themselves. Methods Data from 2,697 questionnaires were analysed using an AI. The short form of the International Personality Item Pool was used to assess the BFPT. Four of the BFPT scores were employed to predict the fifth one and the procedure was repeated for all of them alternatively. The AI was conservatively tuned to maximize the one-way random intraclass correlation coefficient (ICC) between predicted and real values. Their Pearson’s r was calculated too. The free and open source programming language R was used for all the analyses. Dataset source: Hansson, Isabelle; Berg, Anne Ingeborg; Thorvaldsson, Valgeir (2018), “Can personality predict longitudinal study attrition? Evidence from a population-based sample of older adults”, Mendeley Data, V1, doi: 10.17632/g3jx8zt2t9.1 Results Openness, conscientiousness, extroversion, agreeableness and neuroticism predictions obtained ICC of 0.219, 0.146, 0.306, 0.354, 0.121 and Pearson’s r of 0.254, 0.149, 0.393, 0.446, 0.122 respectively. The results for extroversion and agreeableness were indicative of fair performance. Conclusions AI might be useful to predict personality traits, mainly extroversion and agreeableness. This could be utile in many situations, such as dealing with missing data or deciding whether to formally test someone. Finally, the AI used in this study is freely available, allowing anyone to experiment. https://www.cambridge.org/core/product/identifier/S0924933821011895/type/journal_articletraitspsychometryArtificial IntelligencePersonality |
| spellingShingle | A. Mereu Big five personality traits prediction with AI traits psychometry Artificial Intelligence Personality |
| title | Big five personality traits prediction with AI |
| title_full | Big five personality traits prediction with AI |
| title_fullStr | Big five personality traits prediction with AI |
| title_full_unstemmed | Big five personality traits prediction with AI |
| title_short | Big five personality traits prediction with AI |
| title_sort | big five personality traits prediction with ai |
| topic | traits psychometry Artificial Intelligence Personality |
| url | https://www.cambridge.org/core/product/identifier/S0924933821011895/type/journal_article |
| work_keys_str_mv | AT amereu bigfivepersonalitytraitspredictionwithai |
