Efficient and Effective Case Reject-Accept Filtering: A Study Using Machine Learning
Robert Bevan, Alessandro Torrisi, Katie M. Atkinson, Danushka Bollegala, Frans Coenen · Frontiers in artificial intelligence and applications · 2018
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.