Efficient and Effective Case Reject-Accept Filtering: A Study Using Machine Learning

Coenen, FP ORCID: 0000-0003-1026-6649, Bevan, R, Torrisi, A, Atkinson, Katie ORCID: 0000-0002-5683-4106 and Bollegala, D ORCID: 0000-0003-4476-7003 (2018) Efficient and Effective Case Reject-Accept Filtering: A Study Using Machine Learning. In: JURIX 2018.

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Abstract

The decision whether to accept or reject a new case is a well established task undertaken in legal work. This task frequently necessitates domain knowledge and is consequently resource expensive. In this paper it is proposed that early rejection/ acceptance of at least a proportion of new cases can be effectively achieved without requiring significant human intervention. The paper proposes, and evaluates, five different AI techniques whereby early case reject-accept can be achieved. The results suggest it is possible for at least a proportion of cases to be processed in this way.

Item Type:	Conference or Workshop Item (Unspecified)
Uncontrolled Keywords:	Early Case Accept-Reject, Heuristics, Machine Learning
Depositing User:	Symplectic Admin
Date Deposited:	30 Jan 2019 08:13
Last Modified:	19 Jan 2023 01:05
DOI:	10.3233/978-1-61499-935-5-171
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3031963