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Political Bias in Large Language Models: A Comparative Analysis of ChatGPT-4, Perplexity, Google Gemini, and Claude

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  • Tavishi Choudhary

    (Greenwich High, Greenwich, Connecticut, US)

Abstract

Artificial Intelligence large language models have rapidly gained widespread adoption, sparking discussions on their societal and political impact, especially for political bias and its far-reaching consequences on society and citizens. This study explores the political bias in large language models by conducting a comparative analysis across four popular AI mod-els—ChatGPT-4, Perplexity, Google Gemini, and Claude. This research systematically evaluates their responses to politically charged prompts and questions from the Pew Research Center’s Political Typology Quiz, Political Compass Quiz, and ISideWith Quiz. The findings revealed that ChatGPT-4 and Claude exhibit a liberal bias, Perplexity is more conservative, while Google Gemini adopts more centrist stances based on their training data sets. The presence of such biases underscores the critical need for transparency in AI development and the incorporation of diverse training datasets, regular audits, and user education to mitigate any of these biases. The most significant question surrounding political bias in AI is its consequences, particularly its influence on public discourse, policy-making, and democratic processes. The results of this study advocate for ethical implications for the development of AI models and the need for transparency to build trust and integrity in AI models. Additionally, future research directions have been outlined to explore and address the complex AI bias issue.

Suggested Citation

  • Tavishi Choudhary, 2024. "Political Bias in Large Language Models: A Comparative Analysis of ChatGPT-4, Perplexity, Google Gemini, and Claude," RAIS Conference Proceedings 2022-2024 0451, Research Association for Interdisciplinary Studies.
    • Handle: RePEc:smo:raiswp:0451
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    References listed on IDEAS

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    1. Kate Crawford & Ryan Calo, 2016. "There is a blind spot in AI research," Nature, Nature, vol. 538(7625), pages 311-313, October.
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    Keywords

    Large language models (LLM); Generative AI (GenAI); AI Governance and Policy; Ethical AI Systems;
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