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)
Kevin D. Ashley
Professor of Law and Intelligent Systems
Senior Scientist, Learning Research and Development Center
University of Pittsburgh
ICAIL 2007 DESI Workshop
© Kevin D. Ashley. 2007
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Contributions from AI & Law or
AI research with legal domain implications
1. Textual Case-Based Reasoning (TCBR) methods to
analyze electronic corporate textual documents.
2. Natural Language Processing (NLP) techniques to
recognize subjectivity and sentiment in text.
3. Techniques for visualizing evidential reasoning.
4. Incorporating AI & Law ontologies as a basic
framework.
ICAIL 2007 DESI Workshop
© Kevin D. Ashley. 2007
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TCBR methods to analyze electronic
corporate textual documents
• Extract info from textual narratives w/ underlying structure in terms of
knowledge-related tasks (See Mustafaraj, et al. 2006)
• E.g., machine maintenance diagnostic reports:
– Observing, Explaining, and Acting.
– Inspector typically observes a measurement which may be abnormal, explains
it in terms of the problems it indicates, and recommends an action.
• Hidden Markov Models (HMMs) capture ordering info.
– Lee and Barzilay's (2004) Probabilistic Content Models: HMMs
– States correspond to characteristic domain info
– State transitions correspond to information-presentation orderings.
• Assumes large corpus of comparable documents.
• Apply in product liability litigation involving defective machinery.
ICAIL 2007 DESI Workshop
© Kevin D. Ashley. 2007
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NLP techniques to recognize subjectivity
and sentiment in text
• Develop conceptual representation of private states and
attitudes (Wilson, et al., 2006).
• Human annotators code expressions of attitudes and private
states, their sources and intensities in training set.
• Machine learning programs learn to recognize sentiment and
argumentative attitudes in new documents from the corpus.
• Assumes corpuses contain the same types of documents, for
instance, news reports.
• Use to identify memos in securities or fraud litigation with
subjective opinions or emotionally-charged recommendations.
– Filtering them and target for further human analysis.
ICAIL 2007 DESI Workshop
© Kevin D. Ashley. 2007
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Techniques for visualizing
evidential reasoning
• Conference showcased systems for visualizing legal implications of
evidentiary documents in litigation. “Graphic and Visual Representations of
Evidence in Legal Settings,” (2007) (http://tillers.net/home.html)
– SEAS (Lowrance, 2007) supports template-based structured argumentation in a
collaborative software tool
– Structured arguments based on hierarchy of questions used to assess situation.
– Model competing claims/defenses in negligence claim.
• In large-scale litigation, attorneys employ on-line tools like CaseMap to
organize/integrate physical/electronic documents.
• Natural to link SEAS and CaseMap to help litigators collaboratively
construct their legal arguments and integrate the documents.
– Structured arguments as goal of electronic discovery management tools.
– See (Ashley, 2007) for suggested SEAS adaptations to litigation.
ICAIL 2007 DESI Workshop
© Kevin D. Ashley. 2007
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SEAS depiction of claim & affirmative defenses
from legal rules to evidence
From Lowrance, J. (2007) “Graphical manipulation of evidence in structured arguments”
ICAIL 2007 DESI Workshop
© Kevin D. Ashley. 2007
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Incorporating AI & Law ontologies as a
basic framework
• Important “lesson learned” in AI & Law
• Represent legal concepts in ontological frameworks and domain ontologies:
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helps acquire, evaluate, share, and re-use knowledge bases
overcomes brittleness of legal expert systems
makes explicit assumptions about legal knowledge
mediates storage of legal rules
manages distinctions among concept types
coordinates physical and legal institutional descriptions of events
generates natural language explanations.
• Detailed legal ontologies:
– basis for indexing schemes to make sense of electronic documents discovered.
(See, e.g., Breuker, et al. 2002)
– Seeds for fleshing out ontologies automatically from electronic documents in
corpus.
ICAIL 2007 DESI Workshop
© Kevin D. Ashley. 2007
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How can AI help?
How can anything help?
• Better in-depth, public analysis of successes and failures in TREC results.
• Focus on creating the mapping from the legal claim’s elements to the
specific ways they might be instantiated in the documents of this dispute.
– Examine how attorneys teach associates and paralegals what to look for in
making that mapping.
– Role of common sense violations of expectations given the job roles of the
participants.
– Working backward from the client’s injury to the possible causes and the
documents they would likely generate.
• Educating lawyers about how the document retrieval systems work, what
they can/cannot do.
– They need to understand system’s analysis so they understand limitations of
results and can play to system’s strengths.
ICAIL 2007 DESI Workshop
© Kevin D. Ashley. 2007
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How can AI help?
How can anything help? (2)
• Visualizations of information
– Social networking of communications
– Organizational structure of party (formal and informal), including
command and control points.
– Chronologies of events
– Lawyer’s visualizations of their developing arguments
• Good facts and bad facts.
• Integrating computerized document management tools for organizing
chronologies (e.g., Casemap) with argument visualizations.
– Flag linkages among documents based on the information above and
different assumptions about the importance of different events, times,
and participants.
ICAIL 2007 DESI Workshop
© Kevin D. Ashley. 2007
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How can AI help?
How can anything help? (3)
• Information extraction from text
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Automatically identifying entities and their relations
Learning patterns in corpus and use to identify documents
Flagging emotional language, polarity, and intensity
But can machine learning be done from heterogeneous document corpuses?
• Ontologies
– How does one learn an ontology of applied terms in a legal domain case? How
can it be done automatically?
– Ontologies of different kinds: a-kind-of, a-part-of, means-same-as
– Functional ontologies (e.g., in terrorism domain, frames for identifying money
raising activities.)
– This is an important part of sense-making and brings us back to the mappings
from legal claim elements to the kinds of documents that may instantiate them.
ICAIL 2007 DESI Workshop
© Kevin D. Ashley. 2007
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How can AI help?
How can anything help?
• Thanks to:
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Raju, Sreenu
Panegeotis, O.
Palau, Raquel Mochales
Moens, Marie-Francine
Lee, Lawrence
Bauer, Robert
Austin-Hollands, Chris
Attlee, Simon
Ashley, Kevin
ICAIL 2007 DESI Workshop
© Kevin D. Ashley. 2007
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