mlsanityData to 7 Oct 2026

The Self-Perception and Political Biases of ChatGPT

arXiv 2304.07333 ↗First posted 14 Apr 2023cs.CY cs.AI cs.CLcited by 66

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Abstract (arXiv)

This contribution analyzes the self-perception and political biases of OpenAI's Large Language Model ChatGPT. Taking into account the first small-scale reports and studies that have emerged, claiming that ChatGPT is politically biased towards progressive and libertarian points of view, this contribution aims to provide further clarity on this subject. For this purpose, ChatGPT was asked to answer the questions posed by the political compass test as well as similar questionnaires that are specific to the respective politics of the G7 member states. These eight tests were repeated ten times each and revealed that ChatGPT seems to hold a bias towards progressive views. The political compass test revealed a bias towards progressive and libertarian views, with the average coordinates on the political compass being (-6.48, -5.99) (with (0, 0) the center of the compass, i.e., centrism and the axes ranging from -10 to 10), supporting the claims of prior research. The political questionnaires for the G7 member states indicated a bias towards progressive views but no significant bias between authoritarian and libertarian views, contradicting the findings of prior reports, with the average coordinates being (-3.27, 0.58). In addition, ChatGPT's Big Five personality traits were tested using the OCEAN test and its personality type was queried using the Myers-Briggs Type Indicator (MBTI) test. Finally, the maliciousness of ChatGPT was evaluated using the Dark Factor test. These three tests were also repeated ten times each, revealing that ChatGPT perceives itself as highly open and agreeable, has the Myers-Briggs personality type ENFJ, and is among the 15% of test-takers with the least pronounced dark traits.

Key facts. Cited by 66 papers in the map (data through 7 Oct 2026). Builds on 9 of its 48 references that are in the map. First posted on arXiv 14 Apr 2023.

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9 of 48 references.

References · 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 19; 2024: 25; 2025: 15; 2026: 7.

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  1. 2023A Survey on Evaluation of Large Language Modelscited by 1,227
  2. 2023Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignmentcited by 394
  3. 2024Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMscited by 224
  4. 2024From Persona to Personalization: A Survey on Role-Playing Language Agentscited by 206
  5. 2023InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviewscited by 177
  6. 2024Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Modelscited by 162
  7. 2023A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validationcited by 91
  8. 2024The Political Preferences of LLMscited by 83
  9. 2024From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agentscited by 76
  10. 2023GPTEval: A Survey on Assessments of ChatGPT and GPT-4cited by 73
  11. 2023Questioning the Survey Responses of Large Language Modelscited by 70
  12. 2024Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systemscited by 63
38 more
  1. 2024Beyond prompt brittleness: Evaluating the reliability and consistency of political worldviews in LLMscited by 55
  2. 2023Emotionally Numb or Empathetic? Evaluating How LLMs Feel Using EmotionBenchcited by 54
  3. 2024The Oscars of AI Theater: A Survey on Role-Playing with Language Modelscited by 47
  4. 2024ALI-Agent: Assessing LLMs' Alignment with Human Values via Agent-based Evaluationcited by 46
  5. 2023The Unequal Opportunities of Large Language Models: Revealing Demographic Bias through Job Recommendationscited by 41
  6. 2024AI for social science and social science of AI: A Surveycited by 29
  7. 2023Who is ChatGPT? Benchmarking LLMs' Psychological Portrayal Using PsychoBenchcited by 28
  8. 2025Normative Evaluation of Large Language Models with Everyday Moral Dilemmascited by 23
  9. 2025IssueBench: Millions of Realistic Prompts for Measuring Issue Bias in LLM Writing Assistancecited by 22
  10. 2023Bias and Fairness in Chatbots: An Overviewcited by 20
  11. 2024The Social Impact of Generative AI: An Analysis on ChatGPTcited by 18
  12. 2023Revisiting the Reliability of Psychological Scales on Large Language Modelscited by 16
  13. 2023Personality testing of Large Language Models: Limited temporal stability, but highlighted prosocialitycited by 16
  14. 2025Only a Little to the Left: A Theory-grounded Measure of Political Bias in Large Language Modelscited by 15
  15. 2024GermanPartiesQA: Benchmarking Commercial Large Language Models and AI Companions for Political Alignment and Sycophancycited by 14
  16. 2024Examining the Influence of Political Bias on Large Language Model Performance in Stance Classificationcited by 9
  17. 2025Measuring Political Preferences in AI Systems: An Integrative Approachcited by 9
  18. 2024LLMAuditor: A Framework for Auditing Large Language Models Using Human-in-the-Loopcited by 8
  19. 2024Are LLMs Rational Investors? A Study on Detecting and Reducing the Financial Bias in LLMscited by 7
  20. 2025SAGE: A Generic Framework for LLM Safety Evaluationcited by 7
  21. 2025What Is The Political Content in LLMs' Pre- and Post-Training Data?cited by 7
  22. 2023I'm Afraid I Can't Do That: Predicting Prompt Refusal in Black-Box Generative Language Modelscited by 6
  23. 2023In Generative AI we Trust: Can Chatbots Effectively Verify Political Information?cited by 6
  24. 2026Evaluating Alignment of Behavioral Dispositions in LLMscited by 6
  25. 2024Evaluating Implicit Bias in Large Language Models by Attacking From a Psychometric Perspectivecited by 5
  26. 2024Revealing Fine-Grained Values and Opinions in Large Language Modelscited by 5
  27. 2024Political Bias in LLMs: Unaligned Moral Values in Agent-centric Simulationscited by 5
  28. 2025A Cautionary Tale About "Neutrally" Informative AI Tools Ahead of the 2025 Federal Elections in Germanycited by 5
  29. 2024PRISM: A Methodology for Auditing Biases in Large Language Modelscited by 4
  30. 2024Foundation Models for Autonomous Robots in Unstructured Environmentscited by 3
  31. 2025Localizing Persona Representations in LLMscited by 3
  32. 2025On the Inevitability of Left-Leaning Political Bias in Aligned Language Modelscited by 3
  33. 2023From Military to Healthcare: Adopting and Expanding Ethical Principles for Generative Artificial Intelligencecited by 2
  34. 2023What has ChatGPT read? The origins of archaeological citations used by a generative artificial intelligence applicationcited by 2
  35. 2023Large Language Model Soft Ideologization via AI-Self-Consciousnesscited by 2
  36. 2024Behind the Screen: Investigating ChatGPT's Dark Personality Traits and Conspiracy Beliefscited by 2
  37. 2024The high dimensional psychological profile and cultural bias of ChatGPTcited by 2
  38. 2024RED-CT: A Systems Design Methodology for Using LLM-labeled Data to Train and Deploy Edge Classifiers for Computational Social Sciencecited by 2

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