Identifying the factors of suspicion
DOI
10.3233/FAIA200870
Document Type
Conference Paper
Publication Date
12-1-2020
Publication Title
Frontiers in Artificial Intelligence and Applications
Volume
334
First Page
227
Last Page
230
ISSN
9226389
Keywords
Decision tree, Information retrieval, K-nearest neighbor, Logistic regression, Reasonable suspicion, Totality of the circumstances
Abstract
Probable cause determinations are problematic. Like all court decisions using totality-of-the-circumstances tests, it is difficult to use one decision-or even a few-to foresee a subsequent outcome. No human is capable of reading all the relevant Fourth Amendment opinions relevant to resolving any search and seizure issue. Machines may be capable of this task and to do so they will need to be able to identify particular types of suspicious factors from the various ways courts describe the factors. This project examines the ability of three machine learning models to examine the relevant text of opinions to identify the suspicious factors courts used to determine whether adequate suspicion existed from an intrusion protected by the Fourth Amendment.
Open Access
Hybrid_Gold
Repository Citation
Gray, M., Oliver, W., & Crivella, A. (2020). Identifying the factors of suspicion. Frontiers in Artificial Intelligence and Applications, 334, 227-230. https://doi.org/10.3233/FAIA200870