Skip to main content
SpringerLink
Log in

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

Artificial Intelligence and Law volume 18, pages 311–320 (2010)Cite this 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.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

References

  • 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 529–535

  • 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/

  • Ashley K, Bridewell W (2007) III-CXT-Medium: tracing information flow to improve legal e-discovery. NSF Proposal No. 0803049. Declined

  • Ashley K, Brüninghaus S (2009) Automatically classifying case texts and predicting outcomes. Artif Intell Law 17(2):125–165

    Article  Google Scholar 

  • 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)

  • 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 141–147

  • 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

  • Breuker J, Valente A, Winkels R (2004) Legal ontologies in knowledge engineering and information management. Artificial intelligence and law, Vol 12(4). Springer, pp 241–277

    Google Scholar 

  • Buchanan B, Headrick T (1970) Some speculation about artificial intelligence and legal reasoning. Stanford Law Rev 23:40–62

    Article  Google Scholar 

  • 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)

  • 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 164–167

  • 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 39–46

  • 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/

  • 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 168–176

  • 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 243–251

  • Hachey B, Grover C (2006) Extractive summarization of legal texts. Artif Intell Law 14:305–345

    Article  Google Scholar 

  • 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)

  • 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 194–201

  • 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)

  • Jackson P, Al-Kofahi K, Tyrrell A, Vachher A (2003) Information extraction from case law and retrieval of prior cases. Artif Intell 150:239–290

    Article  Google Scholar 

  • Kossinets G, Watts D (2006) Empirical analysis of an evolving social network. Science 311:88–90

    Article  MathSciNet  Google Scholar 

  • 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 217–224

  • 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 517–531

  • 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)

  • 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)

  • Racunas SA, Shah NH, Albert I, Fedoroff NV (2004) HyBrow: a prototype system for computer-aided hypothesis evaluation. Bioinformatics 20:i257–i264

    Article  Google Scholar 

  • Schank R (1986) Explanation patterns: understanding mechanically and creatively. Lawrence Erlbaum Associates, Hillsdale

    Google Scholar 

  • Schwartz MF, Wood DCM (1993) Discovering shared interests using graph analysis. Commun ACM 36:78–89

    Article  Google Scholar 

  • Thagard P (1999) How scientists explain disease. Princeton University Press, Princeton

    Google Scholar 

  • 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 70–77

  • 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 1–4

  • Uyttendaele C, Moens M, Dumortier J (1998) SALOMON: automatic abstracting of legal cases for effective access to court decisions. Artif Intell Law 6:59–79

    Article  Google Scholar 

  • Weber R (1998) Intelligent jurisprudence research, Federal University of Santa Catarina, Florianópolis, Brazil, unpublished doctoral dissertation

  • 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

  • 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

Download references

Author information

Authors and Affiliations

  1. School of Law, University of Pittsburgh, Pittsburgh, PA, 15260, USA

    Kevin D. Ashley

  2. Cognitive Systems Laboratory, Center for the Study of Language and Information, Stanford University, Stanford, CA, 94305, USA

    Will Bridewell

Authors
  1. Kevin D. Ashley
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Will Bridewell
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kevin D. Ashley.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

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

Download citation

  • Published:

  • Issue Date:

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

Keywords

search

Navigation