ICAIL 2007 DESI Workshop© Kevin D. Ashley Can AI & Law Contribute to Managing Electronically Stored Information in Discovery Proceedings? Some Points of Tangency Position Paper Kevin D. Ashley Professor of Law and Intelligent Systems Senior Scientist, Learning Research and Development Center University of Pittsburgh ICAIL 2007 Workshop on Supporting Search and Sensemaking for Electronically Stored Information in Discovery Proceedings (DESI)

ICAIL 2007 DESI Workshop© Kevin D. Ashley 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 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 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 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) ( –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 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 Incorporating AI & Law ontologies as a basic framework Important “lesson learned” in AI & Law Represent legal concepts in ontological frameworks and domain ontologies: –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 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 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 How can AI help? How can anything help? (3) Information extraction from text –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 How can AI help? How can anything help? Thanks to: –Raju, Sreenu –Panegeotis, O. –Palau, Raquel Mochales –Moens, Marie-Francine –Lee, Lawrence –Bauer, Robert –Austin-Hollands, Chris –Attlee, Simon –Ashley, Kevin