Can Large Language Models Capture Public Opinion about Global Warming? An Empirical Assessment of Algorithmic Fidelity and Bias

Large language models (LLMs) have demonstrated their potential in social science research by emulating human perceptions and behaviors, a concept referred to as algorithmic fidelity. The LLMs were conditioned on demographics and/or psychological covariates to simulate survey responses. The findings indicate that LLMs can effectively capture presidential voting behaviors but encounter challenges in accurately representing global warming perspectives when relevant covariates are not included. GPT-4 exhibits improved performance when conditioned on both demographics and covariates. However, disparities emerge in LLM estimations of the views of certain groups, with LLMs tending to underestimate worry about global warming among Black Americans. While highlighting the potential of LLMs to aid social science research, these results underscore the importance of meticulous conditioning, model selection, survey question format, and bias assessment when employing LLMs for survey simulation. Further investigation into prompt engineering and algorithm auditing is essential to harness the power of LLMs while addressing their inherent limitations. Keywords: Global warming; large language models; algorithmic fidelity; public opinion 1. Introduction It is very important to measure public opinion about global warming, as these opinions can have considerable influence over policy-making decisions (Bromley-Trujillo & Poe, 2020) and shape public behavior (Doherty & Webler, 2016). A primary method employed by scholars and policymakers for measuring and assessing these opinions is through representative surveys (Berinsky, 2017). However, the extensive time and financial resources required for these surveys can hinder the timely tracking of evolving public opinions about global warming. Resource constraints can also lead to an unintended bias towards majority opinions, potentially neglecting the perspectives of minority groups due to their typically smaller sample sizes in national representative surveys. Nonetheless, understanding diverse public opinion regarding global warming is also vital for climate justice. This understanding can promote equitable decisionmaking, elevate the concerns of vulnerable communities, help align climate policies with democratic principles, build public support, and address disparities in climate change awareness and priorities. Furthermore, understanding the diversity of public opinion can help support a just transition and mobilize support for climate justice initiatives.