Automation of legal sensemaking in e-discovery

  title={Automation of legal sensemaking in e-discovery},
  author={Christopher Hogan and Robert S. Bauer and Dan Brassil},
  journal={Artificial Intelligence and Law},
  • Christopher Hogan, Robert S. Bauer, Dan Brassil
  • Published 2010
  • Computer Science
  • Artificial Intelligence and Law
  • Retrieval of relevant unstructured information from the ever-increasing textual communications of individuals and businesses has become a major barrier to effective litigation/defense, mergers/acquisitions, and regulatory compliance. Such e-discovery requires simultaneously high precision with high recall (high-P/R) and is therefore a prototype for many legal reasoning tasks. The requisite exhaustive information retrieval (IR) system must employ very different techniques than those applicable… CONTINUE READING
    Emerging AI & Law approaches to automating analysis and retrieval of electronically stored information in discovery proceedings
    • 12
    • Highly Influenced
    • PDF
    Information Retrieval for E-Discovery
    • 21
    • PDF
    Foundations and Trends
    • 14
    • PDF
    On Design and Evaluation of High-Recall Retrieval Systems for Electronic Discovery
    • 2
    • Highly Influenced
    Machine Learning in Legal Practice: Notes from Recent History
    Afterword: data, knowledge, and e-discovery


    Publications referenced by this paper.
    The cost structure of sensemaking
    • 679
    • Highly Influential
    • PDF
    Document categorization in legal electronic discovery: computer classification vs. manual review
    • 39
    • Highly Influential
    Automated Legal Sensemaking: The Centrality of Relevance and Intentionality
    • 7
    • PDF
    Making Sense of Sensemaking 2: A Macrocognitive Model
    • 470
    • PDF
    Overview of the TREC 2008 Legal Track
    • 81
    • PDF
    Information Architecture for the World Wide Web
    • 1,301
    • PDF
    TALKING TECH Automated Document Review Proves Its Reliability
    • 9
    • Highly Influential
    • PDF
    Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
    • 2,055
    • PDF