Political Bias in Large Language Models: A Comparative Analysis of ChatGPT-4, Perplexity, Google Gemini, and Claude
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...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10817610/ |
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| author | Tavishi Choudhary |
| author_facet | Tavishi Choudhary |
| author_sort | Tavishi Choudhary |
| collection | DOAJ |
| description | 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 models—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. |
| format | Article |
| id | doaj-art-598fc4218c5f4e38a4f8ebf29c432ba5 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-598fc4218c5f4e38a4f8ebf29c432ba52025-01-24T00:01:26ZengIEEEIEEE Access2169-35362025-01-0113113411137910.1109/ACCESS.2024.352376410817610Political Bias in Large Language Models: A Comparative Analysis of ChatGPT-4, Perplexity, Google Gemini, and ClaudeTavishi Choudhary0https://orcid.org/0009-0000-9653-1735Greenwich High, Greenwich, CT, USAArtificial 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 models—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.https://ieeexplore.ieee.org/document/10817610/Large language models (LLM)generative AI (GenAI)AI governance and policyethical AI systems |
| spellingShingle | Tavishi Choudhary Political Bias in Large Language Models: A Comparative Analysis of ChatGPT-4, Perplexity, Google Gemini, and Claude IEEE Access Large language models (LLM) generative AI (GenAI) AI governance and policy ethical AI systems |
| title | Political Bias in Large Language Models: A Comparative Analysis of ChatGPT-4, Perplexity, Google Gemini, and Claude |
| title_full | Political Bias in Large Language Models: A Comparative Analysis of ChatGPT-4, Perplexity, Google Gemini, and Claude |
| title_fullStr | Political Bias in Large Language Models: A Comparative Analysis of ChatGPT-4, Perplexity, Google Gemini, and Claude |
| title_full_unstemmed | Political Bias in Large Language Models: A Comparative Analysis of ChatGPT-4, Perplexity, Google Gemini, and Claude |
| title_short | Political Bias in Large Language Models: A Comparative Analysis of ChatGPT-4, Perplexity, Google Gemini, and Claude |
| title_sort | political bias in large language models a comparative analysis of chatgpt 4 perplexity google gemini and claude |
| topic | Large language models (LLM) generative AI (GenAI) AI governance and policy ethical AI systems |
| url | https://ieeexplore.ieee.org/document/10817610/ |
| work_keys_str_mv | AT tavishichoudhary politicalbiasinlargelanguagemodelsacomparativeanalysisofchatgpt4perplexitygooglegeminiandclaude |