Automatic Identification and Empirical Analysis of Legally Relevant Factors

Morgan A. Gray, Jaromír Savelka, Wesley M. Oliver, Kevin D. Ashley

Abstract: This research addresses how to automatically identify certain factors in the texts of legal decisions and analyze their role in courts' decisions. It focuses on drug interdiction auto stop cases in which courts decide whether police officers have reasonable suspicion to detain a motorist. It illustrates how the methods to identify factors automatically can support empirical legal research in the domain and how machine learning methods of different accuracy and interpretability can be harnessed to explain case outcomes in terms legal professionals can understand.