In the course of legal reasoning—whether for purposes of deciding an issue, justifying a decision, predicting how an issue will be decided, or arguing for how it should be decided—one often is required to reach conclusions based on a balance of reasons that is not straightforwardly reducible to the application of rules. Recent AI and Law work has modeled reason-balancing, both within and across cases, with set-theoretic and rule- or value-ordering approaches. This article explores a way to model balancing in (...) quantitative terms that may yield new questions, insights, and tools. (shrink)
The first issue of _Artificial Intelligence and Law_ journal was published in 1992. This paper discusses several topics that relate more naturally to groups of papers than a single paper published in the journal: ontologies, reasoning about evidence, the various contributions of Douglas Walton, and the practical application of the techniques of AI and Law.
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.
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. (shrink)
Document assembly and other substantive legal practice applications are the most knowledge-intense forms of software now widely available in the legal technology marketplace. This article provides an illustrative look at two contemporary practice system engines-CAPS and Scrivener-and examines their relevance for AI-and-law researchers.
Contemporary law offices use many different technologies for storing and retrieving documents produced in the course of legal work. This article examines two approaches in detail: document management, as exemplified by SoftSolutions, and electronic publishing, as exemplified by Folio VIEWS. Some other approaches are reviewed, and the pragmatics, politics, economics, and legalities of legal work product retrieval are discussed.