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

Article

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 

References

  1. Agrawal R, Rajagopalan S, Srikant R, Xu Y (2003) Mining newsgroups using networks arising from social behavior. In: Proceedings of the twelfth international conference on world wide web, ACM Press, Budapest, Hungary, pp 529–535Google Scholar
  2. Ashley K (2007) Can AI & Law contribute to managing electronically stored information in discovery proceedings? Some points of tangency; position paper; ICAIL 2007 workshop on supporting search and sensemaking for electronically stored information in discovery proceedings (DESI). Stanford. http://www.umiacs.umd.edu/~oard/desi-ws/
  3. Ashley K, Bridewell W (2007) III-CXT-Medium: tracing information flow to improve legal e-discovery. NSF Proposal No. 0803049. DeclinedGoogle Scholar
  4. Ashley K, Brüninghaus S (2009) Automatically classifying case texts and predicting outcomes. Artif Intell Law 17(2):125–165CrossRefGoogle Scholar
  5. Attfield S, Blandford A (2010) Discovery-led refinement in e-discovery investigations: sensemaking, cognitive ergonomics and system design. Artificial intelligence and law special issue on e-discovery (this issue)Google Scholar
  6. Baron JR, Thompson P (2007) The search problem posed by large heterogeneous data sets in litigation: possible future approaches to research. In: Proceedings of the eleventh international conference on artificial intelligence and law, ACM Press, Stanford, CA, pp 141–147Google Scholar
  7. Bauer R, Jade T, Hedin B, Hogan C (2008) Automated legal sensemaking: the centrality of relevance and intentionality. In: Proceedings 2nd intl. workshop on supporting search & sensemaking for electronically stored information in discovery, London, June. http://eprints.ucl.ac.uk/9131/1/9131.pdf
  8. Breuker J, Valente A, Winkels R (2004) Legal ontologies in knowledge engineering and information management. Artificial intelligence and law, Vol 12(4). Springer, pp 241–277Google Scholar
  9. Buchanan B, Headrick T (1970) Some speculation about artificial intelligence and legal reasoning. Stanford Law Rev 23:40–62CrossRefGoogle Scholar
  10. Conrad J (2010) E-Discovery revisited: the need for artificial intelligence beyond information retrieval. Artificial intelligence and law special issue on e-discovery (this issue)Google Scholar
  11. Curran J, Clark S (2003) Language independent NER using a maximum entropy tagger. In: Proceedings of the seventh conference on natural language learning (CoNLL), pp 164–167Google Scholar
  12. Daniels J, Rissland E (1997) Finding legally relevant passages in case opinions. In: Proceedings of the sixth international conference on artificial intelligence and law, ACM Press, Melbourne, Australia, pp 39–46Google Scholar
  13. Eichmann D, Chin S-C (2007) Concepts, semantics, and syntax in E-Discovery. In: ICAIL 2007 workshop on supporting search and sensemaking for electronically stored information in discovery proceedings (DESI Workshop) http://www.umiacs.umd.edu/~oard/desi-ws/
  14. Gonçalves T, Quaresma P (2005) Is linguistic information relevant for the text legal classification problem? In: Proceedings of the tenth international conference on artificial intelligence and law, ACM Press, Bologna, Italy, pp 168–176Google Scholar
  15. Grover C, Hachey B, Hughson I, Korycinski C (2003) Automatic summarisation of legal documents. In: Proceedings of ninth international conference on artificial intelligence and law, ACM Press, Edinburgh, Scotland, pp 243–251Google Scholar
  16. Hachey B, Grover C (2006) Extractive summarization of legal texts. Artif Intell Law 14:305–345CrossRefGoogle Scholar
  17. Henseler H (2010) Network-based filtering for large email collections in e-discovery. In: Artificial intelligence and law special issue on e-discovery (this issue)Google Scholar
  18. Hogan C, Bauer R, Brassil D (2009) Human-aided computer cognition for e-discovery. In: Proceedings of 12th international conference on artificial intelligence and law (ICAIL-09), Barcelona, pp 194–201Google Scholar
  19. Hogan C, Bauer R, Brassil D (2010) Automation of legal sensemaking in e-discovery. In: Artificial intelligence and law special issue on e-discovery (this issue)Google Scholar
  20. Jackson P, Al-Kofahi K, Tyrrell A, Vachher A (2003) Information extraction from case law and retrieval of prior cases. Artif Intell 150:239–290CrossRefGoogle Scholar
  21. Kossinets G, Watts D (2006) Empirical analysis of an evolving social network. Science 311:88–90CrossRefMathSciNetGoogle Scholar
  22. McCarty LT (2007) Deep semantic interpretations of legal texts. In: Proceedings of the eleventh international conference on artificial intelligence and law, ACM Press, Stanford, CA, pp 217–224Google Scholar
  23. Mustafaraj E, Hoof M, Freisleben B (2007) Knowledge extraction and summarization for an application of textual case-based interpretation. In: Weber RO, Richter MM (eds) Proceedings of the 6th ICCBR conference, ICCBR 2007, LNAI 4626, pp 517–531Google Scholar
  24. Oard D, Baron J, Hedin B, Lewis D, Tomlinson S (2010) Evaluation of information retrieval for e-discovery. Artificial intelligence and law special issue on e-discovery (this issue)Google Scholar
  25. Privault C, O’Neill J, Ciriza V, Renders J-M (2010) A new tangible user interface for machine learning document review. In: Artificial intelligence and law special issue on e-discovery (this issue)Google Scholar
  26. Racunas SA, Shah NH, Albert I, Fedoroff NV (2004) HyBrow: a prototype system for computer-aided hypothesis evaluation. Bioinformatics 20:i257–i264CrossRefGoogle Scholar
  27. Schank R (1986) Explanation patterns: understanding mechanically and creatively. Lawrence Erlbaum Associates, HillsdaleGoogle Scholar
  28. Schwartz MF, Wood DCM (1993) Discovering shared interests using graph analysis. Commun ACM 36:78–89CrossRefGoogle Scholar
  29. Thagard P (1999) How scientists explain disease. Princeton University Press, PrincetonGoogle Scholar
  30. Thompson P (2001) Automatic categorization of case law. In: Proceedings of the eighth international conference on artificial intelligence and law, ACM Press, St. Louis, MO, pp 70–77Google Scholar
  31. Tillers P (ed) (2007) Special issue on graphic and visual representations of evidence and inference in legal settings. In: Law, Probability, and Risk, vol 6, pp 1–4Google Scholar
  32. Uyttendaele C, Moens M, Dumortier J (1998) SALOMON: automatic abstracting of legal cases for effective access to court decisions. Artif Intell Law 6:59–79CrossRefGoogle Scholar
  33. Weber R (1998) Intelligent jurisprudence research, Federal University of Santa Catarina, Florianópolis, Brazil, unpublished doctoral dissertationGoogle Scholar
  34. Wilson T (2008) Fine-grained subjectivity and sentiment analysis: recognizing the intensity, polarity, and attitudes of private states. Ph. D. Dissertation, University of Pittsburgh Intelligent Systems Program. http://homepages.inf.ed.ac.uk/twilson/dissertation.html
  35. Withers K (2006) Electronically stored information: The December 2006 amendments to the federal rules of civil procedure. Northwestern Journal of Technology & Intellectual Property, 4, (Property 171) at http://www.law.northwestern.edu/journals/njtip/v4/n2/3

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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