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Introduction: Judicial Applications of Artificial Intelligence

Introduction: Judicial Applications of Artificial Intelligence 106 G. SARTOR AND L. KARL BRANTING mated (as stressed by Weizenbaum 1976, Gardner 1987, Berman & Hafner 1989, among others). However, AI research projects in this field have consistently abjured any attempt to usurp the discretionary reasoning of judges. Rather than aiming at the impossible dream (or nightmare) of building an automatic judge, AI research has aimed at developing practical tools to support judicial activities as well as new analytical tools for understanding and modeling judicial decision-making. 1. Modeling judicial tasks No form of legal reasoning seems to depend more heavily on uniquely human abilities than the decision-making of a judge. Judicial decision-making requires assessing the credibility of witnesses, evaluating the probative weight of evidence, interpreting the meaning and intended effect of legal statutes and other normative authorities and, especially in criminal cases, balancing mercy with justice. How can AI contribute to a process that encompasses such a wide range of knowledge, judgment, and experience? The answer to this question, like the answer to the general AI problem of build- ing intelligent artificial agents, is that one must begin by identifying the individual tasks that collectively constitute the overall task of judicial problem solving. The information-processing requirements of http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence and Law Springer Journals

Introduction: Judicial Applications of Artificial Intelligence

Artificial Intelligence and Law , Volume 6 (4) – Oct 16, 2004

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Publisher
Springer Journals
Copyright
Copyright © 1998 by Kluwer Academic Publishers
Subject
Computer Science; Artificial Intelligence (incl. Robotics); International IT and Media Law, Intellectual Property Law; Philosophy of Law; Legal Aspects of Computing; Information Storage and Retrieval
ISSN
0924-8463
eISSN
1572-8382
DOI
10.1023/A:1008223408127
Publisher site
See Article on Publisher Site

Abstract

106 G. SARTOR AND L. KARL BRANTING mated (as stressed by Weizenbaum 1976, Gardner 1987, Berman & Hafner 1989, among others). However, AI research projects in this field have consistently abjured any attempt to usurp the discretionary reasoning of judges. Rather than aiming at the impossible dream (or nightmare) of building an automatic judge, AI research has aimed at developing practical tools to support judicial activities as well as new analytical tools for understanding and modeling judicial decision-making. 1. Modeling judicial tasks No form of legal reasoning seems to depend more heavily on uniquely human abilities than the decision-making of a judge. Judicial decision-making requires assessing the credibility of witnesses, evaluating the probative weight of evidence, interpreting the meaning and intended effect of legal statutes and other normative authorities and, especially in criminal cases, balancing mercy with justice. How can AI contribute to a process that encompasses such a wide range of knowledge, judgment, and experience? The answer to this question, like the answer to the general AI problem of build- ing intelligent artificial agents, is that one must begin by identifying the individual tasks that collectively constitute the overall task of judicial problem solving. The information-processing requirements of

Journal

Artificial Intelligence and LawSpringer Journals

Published: Oct 16, 2004

References