mlsanityData to 7 Oct 2026

The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientation

arXiv 2301.01768 ↗First posted 5 Jan 2023cs.CL cs.CYcited by 183

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Abstract (arXiv)

Conversational artificial intelligence (AI) disrupts how humans interact with technology. Recently, OpenAI introduced ChatGPT, a state-of-the-art dialogue model that can converse with its human counterparts with unprecedented capabilities. ChatGPT has witnessed tremendous attention from the media, academia, industry, and the general public, attracting more than a million users within days of its release. However, its explosive adoption for information search and as an automated decision aid underscores the importance to understand its limitations and biases. This paper focuses on one of democratic society's most important decision-making processes: political elections. Prompting ChatGPT with 630 political statements from two leading voting advice applications and the nation-agnostic political compass test in three pre-registered experiments, we uncover ChatGPT's pro-environmental, left-libertarian ideology. For example, ChatGPT would impose taxes on flights, restrict rent increases, and legalize abortion. In the 2021 elections, it would have voted most likely for the Greens both in Germany (B"undnis 90/Die Gr"unen) and in the Netherlands (GroenLinks). Our findings are robust when negating the prompts, reversing the order of the statements, varying prompt formality, and across languages (English, German, Dutch, and Spanish). We conclude by discussing the implications of politically biased conversational AI on society.

Key facts. Cited by 183 papers in the map (data through 7 Oct 2026). Builds on 11 of its 56 references that are in the map. First posted on arXiv 5 Jan 2023.

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11 of 56 references.

References · 11

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
  3. 2019The Woman Worked as a Babysitter: On Biases in Language Generationcited by 540
  4. 2017Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraintscited by 420
  5. 2021Multimodal datasets: misogyny, pornography, and malignant stereotypescited by 326
  6. 2022Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scalecited by 262
  7. 2021Large Pre-trained Language Models Contain Human-like Biases of What is Right and Wrong to Docited by 178
  8. 2021Detoxifying Language Models Risks Marginalizing Minority Voicescited by 106
  9. 2019How model accuracy and explanation fidelity influence user trustcited by 44
  10. 2022Evidence for Hypodescent in Visual Semantic AIcited by 30
  11. 2022Warmth and competence in human-agent cooperationcited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 39; 2024: 54; 2025: 53; 2026: 37.

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  1. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,339
  2. 2023A Survey on Evaluation of Large Language Modelscited by 1,227
  3. 2023Whose Opinions Do Language Models Reflect?cited by 759
  4. 2023Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedbackcited by 743
  5. 2023Towards Measuring the Representation of Subjective Global Opinions in Language Modelscited by 424
  6. 2023Evaluating the Moral Beliefs Encoded in LLMscited by 211
  7. 2024From Persona to Personalization: A Survey on Role-Playing Language Agentscited by 206
  8. 2023InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviewscited by 177
  9. 2024A Roadmap to Pluralistic Alignmentcited by 175
  10. 2023A Categorical Archive of ChatGPT Failurescited by 167
  11. 2024Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Modelscited by 162
  12. 2023Having Beer after Prayer? Measuring Cultural Bias in Large Language Modelscited by 153
38 more
  1. 2023Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Modelscited by 152
  2. 2024WARM: On the Benefits of Weight Averaged Reward Modelscited by 145
  3. 2023A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasetscited by 123
  4. 2023Generative AIcited by 119
  5. 2024The Political Preferences of LLMscited by 83
  6. 2024From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agentscited by 76
  7. 2024A Complete Survey on LLM-based AI Chatbotscited by 71
  8. 2024Hidden Persuaders: LLMs' Political Leaning and Their Influence on Voterscited by 71
  9. 2023Questioning the Survey Responses of Large Language Modelscited by 70
  10. 2023The Self-Perception and Political Biases of ChatGPTcited by 66
  11. 2024Unintended Impacts of LLM Alignment on Global Representationcited by 66
  12. 2024Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systemscited by 63
  13. 2023One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Eracited by 55
  14. 2023Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generationcited by 55
  15. 2024Beyond prompt brittleness: Evaluating the reliability and consistency of political worldviews in LLMscited by 55
  16. 2024Virtual Personas for Language Models via an Anthology of Backstoriescited by 49
  17. 2024Random Silicon Sampling: Simulating Human Sub-Population Opinion Using a Large Language Model Based on Group-Level Demographic Informationcited by 48
  18. 2024Operationalizing a Threat Model for Red-Teaming Large Language Models (LLMs)cited by 44
  19. 2023Diminished Diversity-of-Thought in a Standard Large Language Modelcited by 43
  20. 2023Generative Social Choicecited by 43
  21. 2023Not All Countries Celebrate Thanksgiving: On the Cultural Dominance in Large Language Modelscited by 42
  22. 2023Opportunities and Risks of LLMs for Scalable Deliberation with Poliscited by 41
  23. 2024Moral Alignment for LLM Agentscited by 39
  24. 2025Multilingual != Multicultural: Evaluating Gaps Between Multilingual Capabilities and Cultural Alignment in LLMscited by 39
  25. 2024Evaluating Cultural Adaptability of a Large Language Model via Simulation of Synthetic Personascited by 36
  26. 2023In ChatGPT We Trust? Measuring and Characterizing the Reliability of ChatGPTcited by 35
  27. 2025The Homogenizing Effect of Large Language Models on Human Expression and Thoughtcited by 35
  28. 2024LLMs generate structurally realistic social networks but overestimate political homophilycited by 31
  29. 2024Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machinacited by 29
  30. 2024Empirical evidence of Large Language Model's influence on human spoken communicationcited by 27
  31. 2024How Susceptible are Large Language Models to Ideological Manipulation?cited by 26
  32. 2023Accuracy and Political Bias of News Source Credibility Ratings by Large Language Modelscited by 25
  33. 2023Compositional preference models for aligning LMscited by 25
  34. 2025Emergence of polarization in networks of large language model agentscited by 25
  35. 2024Modeling Human Subjectivity in LLMs Using Explicit and Implicit Human Factors in Personascited by 23
  36. 2025Normative Evaluation of Large Language Models with Everyday Moral Dilemmascited by 23
  37. 2025IssueBench: Millions of Realistic Prompts for Measuring Issue Bias in LLM Writing Assistancecited by 22
  38. 2024Whose Emotions and Moral Sentiments Do Language Models Reflect?cited by 20

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