Artificial Intelligence and Law

, Volume 18, Issue 4, pp 311–320

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


DOI: 10.1007/s10506-010-9098-4

Cite this article as:
Ashley, K.D. & Bridewell, W. Artif Intell Law (2010) 18: 311. doi:10.1007/s10506-010-9098-4


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.


E-discovery Litigators’ relevance hypotheses User modeling Machine learning Social networks Legal ontologies 

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.School of LawUniversity of PittsburghPittsburghUSA
  2. 2.Cognitive Systems Laboratory, Center for the Study of Language and InformationStanford UniversityStanfordUSA

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