Artificial Intelligence and Law

, Volume 18, Issue 4, pp 481–486 | Cite as

Afterword: data, knowledge, and e-discovery

  • David D. LewisEmail author


Research in Artificial Intelligence (AI) and the Law has maintained an emphasis on knowledge representation and formal reasoning during a period when statistical, data-driven approaches have ascended to dominance within AI as a whole. Electronic discovery is a legal application area, with substantial commercial and research interest, where there are compelling arguments in favor of both empirical and knowledge-based approaches. We discuss the cases for both perspectives, as well as the opportunities for beneficial synergies.


Electronically stored information ESI Automated reasoning Pattern recognition Categorization Quality control 



Many thanks to Kevin Ashley for his helpful feedback. All responsibility for errors remains with me.


  1. Ashley KD, Bridewell W (2010) Emerging AI & Law approaches to automating analysis and retrieval of electronically stored information in discovery proceedings. Artif Intell Law 18. doi: 10.1007/s10506-010-9098-4
  2. Ashley KD, Baron JR, Conrad JG, Light M, Logan D (2008) Cross-border EDiscovery/E-Disclosure workshop (DESI III). ICAIL 2009 workshop proposalGoogle Scholar
  3. Attfield S, Blandford A (2010) Discovery-led refinement in e-discovery investigations: sensemaking, cognitive ergonomics and system design. Artif Intell LawGoogle Scholar
  4. Baron JR, Lewis DD, Oard DW (2006) TREC-2006 legal track overview. In: The 15th text retrieval conference (TREC 2006) Proceedings, National Institute of Standards and Technology, Gaithersburg, MD, pp 79–98Google Scholar
  5. Blair DC, Maron ME (1985) An evaluation of retrieval effectiveness for a full-text document-retrieval system. Commun ACM 28(3):289–299CrossRefGoogle Scholar
  6. Chandrasekar R, Chickering M, Ipeirotis P, Mason W, Provost F (2010) HCOMP ’10: Proceedings of the ACM SIGKDD workshop on human computation. ACM, Washington, DCGoogle Scholar
  7. Church K (2004) Speech and language processing: can we use the past to predict the future? In: Text, speech and dialogue: TSD 2004 Proceedings, Springer, Brno, Czech Republic, LNAI, 3206:3–13Google Scholar
  8. Conrad JG (2010) E-Discovery revisited: the need for artificial intelligence beyond information retrieval. Artif Intell Law 18. doi: 10.1007/s10506-010-9096-6
  9. Dixon L, Gill B (2001) Changes in the standards for admitting expert evidence in federal civil cases since the Daubert decision. RAND Institute for Civil Justice, Santa MonicaGoogle Scholar
  10. Getoor L, Taskar B (2007) Introduction to statistical relational learning. Adaptive computation and machine learning. MIT Press, CambridgeGoogle Scholar
  11. Hayes PJ, Weinstein SP (1990) CONSTRUE-TIS: a system for content-based indexing of a database of news stories. In: Innovative applications of artificial intelligence 2, Washington, DC, pp 49–64Google Scholar
  12. Hendler J (2008) Avoiding another AI winter. IEEE Intell Syst 23(2):2–4CrossRefGoogle Scholar
  13. Hogan C, Bauer RS, Brassil D (2010) Automation of legal sensemaking in e-discovery. Artif Intell Law 18. doi: 10.1007/s10506-010-9100-1
  14. Kershaw A, Howie J (2010) eDiscovery institute survey on predictive coding. Tech. Rep., Electronic Discovery InstituteGoogle Scholar
  15. Lewis DD (1991) Data extraction as text categorization: An experiment with the MUC-3 corpus. In: Proceedings of the 3rd conference on message understanding, San Diego, CA, pp 245–255Google Scholar
  16. Lewis DD, Sebastiani F (2001) Report on the workshop on operational text classification systems (OTC-01). ACM SIGIR Forum 35(2):8–11CrossRefGoogle Scholar
  17. Oard DW, Baron JR, Hedin B, Lewis DD, Tomlinson S (2010) Evaluation of information retrieval for E-discovery. Artif Intell Law 18. doi: 10.1007/s10506-010-9093-9
  18. Privault C, ONeill J, Ciriza V, Renders J (2010) A new tangible user interface for machine learning document review. Artif Intell Law 18. doi: 10.1007/s10506-010-9090-z
  19. Turtle H (1995) Text retrieval in the legal world. Artif Intell Law 3(1–2):5–54CrossRefGoogle Scholar
  20. Verykios VS, Bertino E, Fovino IN, Provenza LP, Saygin Y, Theodoridis Y (2004) State-of-the-art in privacy preserving data mining. ACM SIGMOD Rec 33(1):50–57CrossRefGoogle Scholar
  21. Vleduts-Stokolov N (1987) Concept recognition in an automatic text-processing system for the life sciences. J Am Soc Info Sci 38(4):269–287CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.David D. Lewis ConsultingChicagoUSA

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