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Efficient and Effective Case Reject-Accept Filtering: A Study Using Machine Learning
Robert Bevan, Alessandro Torrisi, Katie Atkinson, Danushka Bollegala, Frans Coenen
171 - 175
10.3233/978-1-61499-935-5-171
Frontiers in Artificial Intelligence and Applications
Volume 313: Legal Knowledge and Information Systems
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.

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