AI's Partisan Lens: A Comparative Study of Bias in Leading Language Models

Abstract

This study presents a comprehensive examination of biases in language models, focusing on OpenAI's GPT-4, Google's Gemini, Mistral's 8x7B, and Meta's Llama 2. The core objective is to understand how these models interpret demographic identities in the context of partisan affiliation predictions. Utilizing the silicon sampling technique, the study created “silicon personas” based on demographic traits from a nationally representative sample and analyzed the models' predictions of these personas' partisan affiliations. The research found that each model exhibits distinct patterns in predicting partisan affiliations based on demographic traits, indicating embedded biases. Our findings reveal that while some models showed a tendency to associate certain demographics with specific political affiliations, others demonstrated a more balanced approach. This variance highlights the complex nature of biases in language models and their potential influence on societal applications such as recruitment, content moderation, and political campaigning. This study emphasizes the importance of understanding and mitigating biases in AI, as these technologies become increasingly integrated into various sectors. This study contributes to the broader discourse on responsible AI development and deployment by examining the nuanced landscape of biases within advanced language models. This underscores the need for continuous scrutiny and refinement of AI tools to ensure that they are equitable and beneficial to society.