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

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.

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Correspondence to Kevin D. Ashley.

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Ashley, K.D., Bridewell, W. Emerging AI & Law approaches to automating analysis and retrieval of electronically stored information in discovery proceedings. Artif Intell Law 18, 311–320 (2010). https://doi.org/10.1007/s10506-010-9098-4

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  • DOI: https://doi.org/10.1007/s10506-010-9098-4

Keywords

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