Abstract: Computational models of relevance in case-based legal reasoning have traditionallybeen based on algorithms for comparing the facts and substantive legal issues of aprior case to those of a new case. In this paper we argue that robust models ofcase-based legal reasoning must also consider the broader social and jurisprudentialcontext in which legal precedents are decided. We analyze three aspects of legalcontext: the teleological relations that connect legal precedents to the socialvalues and policies they serve, the temporal relations between prior andsubsequent cases in a legal domain, and the procedural posture of legal cases,which defines the scope of their precedential relevance. Using real examples drawnfrom appellate courts of New York and Massachusetts, we show with the courts' ownarguments that the doctrine of stare decisis (i.e., similar facts should lead to similar results) is subject to contextual constraints and influences. For each of the three aspects of legal context, we outline an expanded computational framework for case-based legal reasoning that encompasses the reasoning of the examples, and provides a foundation for generating a more robust set of legal arguments.

Abstract: In this paper I recapitulate the ideas of Berman and Hafner (1993) regarding the role of teleology in legal argument. I show how these ideas can be used to address some issues arising from more recent work on legal argument, and how this relates to ideas associated with the "New Rhetoric" of Perelman. I illustrate the points with a discussion of the classic problem of which vehicles should be allowed in parks.

Abstract: In this short note, we discuss several aspectsof "dimensions" and the related constructof "factors". We concentrate on those aspectsthat are relevant to articles in this specialissue, especially those dealing with the analysisof the wild animal cases discussed inBerman and Hafner's 1993 ICAIL article. We reviewthe basic ideas about dimensions,as used in HYPO, and point out differences withfactors, as used in subsequent systemslike CATO. Our goal is to correct certainmisconceptions that have arisen over the years.

Abstract: This paper proposes to model legal reasoning asdialectical theory-constructiondirected by teleology. Precedents are viewed asevidence to be explained throughtheories. So, given a background of factors andvalues, the parties in a case canbuild their theories by using a set of operators,which are called theory constructors.The objective of each party is to provide theoriesthat both explain the evidence (theprecedents) and support the decision wished by thatparty. This leads to theory-basedargumentation, i.e., a dialectical exchange ofcompeting theories, which support opposedoutcomes by explaining the same evidence and appealingto the same values. The winneris the party that can reply with a more coherent theoryto all theories of its adversary.

Abstract: This article is an exercise in computational jurisprudence. It seems clear thatthe field of AI and Law should draw upon the insights of legal philosophers,whenever possible. But can the computational perspective offer anything inreturn? I will explore this question by focusing on the concept of OWNERSHIP,which has been debated in the jurisprudential literature for centuries. Althoughthe intellectual currents here flow mostly in one direction --- from legal philosophy to AI --- I will show that there are also some insights to be gained from a computational analysis of the OWNERSHIP relation. In particular, the article suggests a computational explanation for the emergence of abstract property rights, divorced from concrete material objects.

Abstract: This article describes recent jurisprudential accountsof analogical legal reasoning andcompares them in detail to the computational modelof case-based legal argument inCATO The jurisprudential models provide a theoryof relevance based on low-levellegal principles generated in a process ofcase-comparing reflective adjustment Thejurisprudential critique focuses on the problemsof assigning weights to competingprinciples and dealing with erroneously decidedprecedents CATO, a computerizedinstructional environment, employs ArtificialIntelligence techniques to teach lawstudents how to make basic legal argumentswith cases The computational modelhelps students test legal hypotheses againsta database of legal cases, draws analogiesto problem scenarios from the database, andcomposes arguments by analogy with a setof argument moves The CATO model accountsfor a number of the important featuresof the jurisprudential accounts, includingimplementing a kind of reflective adjustmentIt also avoids some of the problems identifiedin the critique; for instance, it deals withweights in a non-numeric, context-sensitivemanner The article concludes by describingthe contributions AI research can make tojurisprudential investigations of complexcognitive phenomena of legal reasoning Forinstance, unlike the jurisprudential models,CATO provides a detailed account of how togenerate multiple interpretations of a citedcase, downplaying or emphasizing the legalsignificance of distinctions in terms of thepurposes of the law as the argument contextdemands

Abstract: At the Donald Berman Laboratory for Information Technology and Law, La TrobeUniversity Australia, we have been building legal decision support systems for a dozenyears Whilst most of our energy has been devoted to conducting research in ArtificialIntelligence and Law, over the past few years we have increasingly focused uponbuilding legal decision support systems that have a commercial focusIn this paper we discuss the evolution of our systems We begin with a discussion ofrule-based systems and discuss the transition to hybrid rule-based/case-based systemsWe next discuss how we have used machine learning in building legal decision supportsystems Our focus on using machine learning led us to investigate the domains ofexplanation and argumentation We conclude by discussing our current work onbuilding negotiation support systems and tools for constructing web-based legaldecision support systems

Abstract: Business theory suggests that knowledge intensive professionslike law would devote major attention to knowledge management (KM) activities. Afterall, since a firm's combined knowledge is a key differentiating asset, one wouldexpect the exploitation of that asset to be a high priority. Yet new lawyers are oftensurprised at how little of such activities take place within firms. One might also expect tofind rich connections between academic research in knowledge management and law firmsusing that research. The rarity of such connections stands in sharp contrast to thebreadth and depth of use of substantive legal research and analysis. These disappointmentsare not unrelated: a firm that allocates little time to systems for leveraging its intellectualcontent is unlikely to invest in staying up to date with externalresearch relating to such systems.The authors believe that significant progress nonetheless may bemade both in applying KM methodologies to law firm work and better connecting theacademic and practice sectors. To those ends, this article explores three theses: (1)Legal technologists can and should lead by example in utilizing KM tools and methods; (2) Theeconomics of legal practice still pose substantial challenges to even those knowledge technologies considered by some as truly ``disruptive''; and (3) Focussing onareas that could yield a tremendous economic harvest may help forge richer connections betweenthe work being done in academic and practice spheres.

Abstract: Legal contracts and litigation documents common to the American legal system were encoded in the eXtensible Markup Language (XML). XML also represents rules about the contracts and litigation procedure. In addition to an expert system tool that allows one to make inferences with that engine, a Graphical User Interface (GUI) generates the XML representing the rules. A rulebase is developed by marking up examples of the XML to be interpreted and the XML to be generated, analogously to Query By Example. This article provides a broader context of the synergy between XML and artificial intelligence by including discussions of: (1) the role of Artificial Intelligence in handling routine litigation; (2) how the use of XML enables legal expert systems to get their `input' without the user having to enter the same information again for the expert system;(3) the advantages of XML markup over other forms of markup for documents; (4) the relationship between XML and ontologies; (5) other projects using XML with rules or legal affairs.

Abstract: The proliferation of electronic databases is raising someimportant questions about how the evolving access to new or previously inaccessible information is likely to change the practice of law. This paper discusses TRAC, an interesting electronic source of previously inaccessible information that is currently used by members of the media, public interest groups, lawyers, and the federal government. Summaries, reports, and snapshots of TRAC's data can be accessed through a series of public web sites. TRAC's subscription service allows users access to the data warehouse and data mining tools (see http://tracfed.syr.edu/info.html for more information). Additionally the paper examines how AI can be employed to assist for the legal profession in utilization of TRAC's data. Finally, it speculates about how TRAC and other new electronic data sources may impact the practice of law.