Toward Automatically Identifying Legally Relevant Factors
Frontiers in Artificial Intelligence and Applications
Automatic Text Identification, Multi-label Classification, Sentence Classification, Totality of the Circumstances Test
In making legal decisions, courts apply relevant law to facts. While the law typically changes slowly over time, facts vary from case to case. Nevertheless, underlying patterns of fact may emerge. This research focuses on underlying fact patterns commonly present in cases where motorists are stopped for a traffic violation and subsequently detained while a police officer conducts a canine sniff of the vehicle for drugs. We present a set of underlying patterns of fact, that is, factors of suspicion, that police and courts apply in determining reasonable suspicion. We demonstrate how these fact patterns can be identified and annotated in legal cases and how these annotations can be employed to fine-tune a transformer model to identify the factors in previously unseen legal opinions.
Gray, M., Savelka, J., Oliver, W., & Ashley, K. (2022). Toward Automatically Identifying Legally Relevant Factors. Frontiers in Artificial Intelligence and Applications, 362, 53-62. https://doi.org/10.3233/FAIA220448