Empirical legal analysis simplified: reducing complexity through automatic identification and evaluation of legally relevant factors.

Abstract

This paper investigates the potential for reducing the complexity of AI and Law and empirical legal studies projects through a novel annotation methodology that relies on GPT Family Models to assist human annotators. Improving the speed, cost and quality of annotation could greatly benefit such projects. In modelling types of legal claims, researchers in the fields of empirical legal studies and AI and Law have long relied on manually annotating factors in case texts. To demonstrate our methodology, we employ cases and factors regarding whether a police officer has constitutional authority to detain a motorist on the basis of the officer's suspicion that the motorist is trafficking drugs. Our results demonstrate how recent advances in text analytics can reduce the burden of identifying factors in large numbers of cases and improve machine learning models' predictions of case outcomes. This article is part of the theme issue 'A complexity science approach to law and governance'.