An Artificial-Intelligence-Based Semantic Assist Framework for Judicial Trials

Bibliographic Data

ID3793192
AuthorsYaohui Jin (0000-0001-6158-6277), Hao He (0000-0002-6823-9603)
Year2020
Volume7
Issue3
Pages531-540
Publication date2020-10-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueAsian Journal of Law and Society (JOURNAL)
Journal identifiersISSN: 2052-9015 • E-ISSN: 2052-9023
PublisherCambridge University Press (CUP) (PUBLISHER)
DOI10.1017/als.2020.33
OpenAlexW3131509242
LanguageEN
Citations received4
References cited9

Due to their success in routine tasks such as voice recognition, image classification, and text processing, extensive attention has been aroused on how to use artificial intelligence (AI)-based automation tools in the judicial-trial process to improve efficiency. Meanwhile, judicial trial is a complex task that requires accurate insight and subtle analysis of the cases, law, and common knowledge. Applying the results provided by AI-based automation tools directly to the judicial-trial process is controversial due to their irregular logic and low accuracy. Based on this observation, this article investigates the logic underlined in judicial trials and the technical characteristics of AI, and proposes an AI-based semantic assist approach for judicial trials that is logical and transparent to the judges

Automation · Machine learning · Natural language processing · Process (computing · Programming language · Systems engineering · Task (project management · Artificial Intelligence in Law · Computer Science · Engineering · Law, Economics, and Judicial Systems · Legal Education and Practice Innovations · Artificial Intelligence

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