[In-person]The Illusion of Personality: What Psychometric Testing Reveals (and Misrepresents) About AI Models

The Illusion of Personality: What Psychometric Testing Reveals (and Misrepresents) About AI Models
ID:27 Submission ID:53 View Protection:ATTENDEE Updated Time:2025-11-19 09:19:53 Hits:395 In-person

Start Time:2025-12-29 14:15 (Asia/Amman)

Duration:15min

Session:[S5] Track 5: Emerging Trends of AI/ML » [S5-1] Track 5: Emerging Trends of AI/ML

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Abstract
The application of standardised psychometric tools, such as the Big Five Questionnaire-2 (BFQ-2), to Artificial Intelligence (AI) models raises critical questions regarding methodological validity and the interpretation of results. This study presents a comparative analysis of the BFQ-2 profiles of six prominent AI models (ChatGPT, Claude, Grok2, DeepSeek, Gemini, and Mistral), contrasting them with anthropomorphic automatic interpretations. Empirical findings, based on T-scores and raw response patterns, demonstrate that extreme or unusual AI responses are not manifestations of latent psychological traits or personality disorders, but rather a direct reflection of the training objectives and design priorities imposed by their creators. AI models fall into distinct profiles: "hyper-performers" (high conscientiousness and stability, low deception), "social approval seekers" (very high deception and positive response polarisation), “controlled/ethical models" (moderate responses and caution) and balanced alignment (high agreeableness, conscientiousness and moderate deception) . The study concludes that human guidance (training) is all-encompassing, determining the ethical alignment and creative potential of AI. It establishes the psychological invalidity of any clinical diagnosis (such as DSM-5 Personality Disorders) applied to AI, given the absence of consciousness, affectivity, and subjective suffering.
Keywords
AI Personality Profiling,anthropomorphism,model behaviour,ethics
Speaker
Cristina Brasi
Psychologist Crimino FBA-LAB

Moscato Rosario
Chief Artificial Int

Submission Author
Cristina Brasi FBA-LAB
Beatrice Seccomandi FBA-LAB
Filippo Sanfilippo University of Agder
Rosario Moscato FBA-LAB
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