Measuring Political Preferences in AI Systems: An Integrative Approach

Measuring Political Preferences in AI Systems - A n Integrative Approach David Rozado Political biases in Large Language Model (LLM) - based artificial intelligence (AI) systems, such as OpenAI ' s ChatGPT or Google ' s Gemini, have been previously reported . While several prior studies have attempted to quantify these biases using political orientation tests, such approaches are limited by potential tests ' calibration biases and constrained response formats that do not reflect real - world human - AI interaction s. This study employs a multi - method approach to assess political bias in leading AI systems, integrating four complementary methodologies: (1) linguistic comparison of AI - generated text with the language used by Republican and Democratic U.S. Congress mem bers, (2) analysis of political viewpoints embedded in AI - generated policy recommendations, (3) sentiment analysis of AI - generated text toward politically affiliated public figures, and (4) standardized political orientation testing. Results indicate a con sistent left - leaning bias across most contemporary AI systems, with arguably varying degrees of intensity. However, this bias is not an inherent feature of LLMs; prior research demonstrates that fine - tuning with politically skewed data can realign these mo dels across the ideological spectrum. The presence of systematic political bias in AI systems poses risks, including reduced viewpoint diversity, increased societal polarization, and the potential for public mistrust in AI technologies. To mitigate these r isks, AI systems should be designed to prioritize factual accuracy while maintaining neutrality on most lawful normative issues. Furthermore, independent monitoring platforms are necessary to ensure transparency, accountability, and responsible AI developm ent. Introduction Recent advancements in AI technology, exemplified by Large Language Models (LLMs) like ChatGPT, represent one of the most significant technological breakthroughs in recent decades. The ability of AI systems to understand and generate human - like natural language has unlocked new possibilities for automation, human - computer interaction, content generation, and information retrieval. However, th ese impressive capabilities ha ve also raised concerns abo ut the potential biases that such systems might harbor [1], [2], [3], [4] . Preliminary evidence has suggested that AI systems exhibit political biases in the textual content they generate [2], [5], [6] .