Emerging AI & Law approaches to automating analysis and retrieval of electronically stored information in discovery proceedings

Artificial Intelligence and Law 18 (4):311-320 (2010)

Abstract
This article provides an overview of, and thematic justification for, the special issue of the journal of Artificial Intelligence and Law entitled “E-Discovery”. In attempting to define a characteristic “AI & Law” approach to e-discovery, and since a central theme of AI & Law involves computationally modeling legal knowledge, reasoning and decision making, we focus on the theme of representing and reasoning with litigators’ theories or hypotheses about document relevance through a variety of techniques including machine learning. We also identify two emerging techniques for enabling users’ document queries to better express the theories of relevance and connect them to documents: social network analysis and a hypothesis ontology
Keywords E-discovery  Litigators’ relevance hypotheses  User modeling  Machine learning  Social networks  Legal ontologies
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DOI 10.1007/s10506-010-9098-4
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References found in this work BETA

How Scientists Explain Disease.Paul Thagard - 1999 - Princeton University Press.
Extractive Summarisation of Legal Texts.Ben Hachey & Claire Grover - 2006 - Artificial Intelligence and Law 14 (4):305-345.

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Afterword: Data, Knowledge, and E-Discovery. [REVIEW]David D. Lewis - 2010 - Artificial Intelligence and Law 18 (4):481-486.

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