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Unveiling Political Bias in Artificial Intelligence: A Systematic Literature Review
Giacomo Caroli • Luca Mongiello
abstract
Large Language Models (LLMs) have revolutionized the landscape of natural language processing, finding applications across diverse domains. However, the issue of political biases encoded within these models—which can influence outputs and perpetuate ideolog- ical leanings—is often underrated. A recent study, ChatGPT vs. Google: A Comparative Study of Search Performance and User Experience by Xu et al.[1], sheds light on how users perceive and interact with LLM-powered chatbots com- pared to traditional search engines. Their findings reveal that while tools like ChatGPT enhance efficiency and user satisfaction by de- livering concise and accessible information, they may inadvertently encourage overreliance and fail to expose users to diverse perspec- tives, particularly in tasks requiring fact-checking or critical evalu- ation. Building on these insights, this project aims to conduct a systematic literature review (SLR) on political bias in LLMs, focusing on its identification, measurement, and mitigation.
