Using Large Language Models to Measure the Ideology of Supreme Court Justices

Ideology is central to understanding the behavior of U.S. Supreme Court Justices. The two most widely used measures of judicial ideology—Martin–Quinn and Segal–Cover scores—each have limitations. We propose an alternative approach that leverages large language models (LLMs) to estimate ideology scores for Justices from 1925 to the present. Using ChatGPT-5, we generate static and dynamic scores for Justices identified as shifting ideologically. Our scores strongly correlate with existing measures but are not mere reproductions, and they exhibit high replicability across iterations. These findings suggest LLMs provide a promising new tool for generating valid and reliable measures of judicial ideology.