SystemsAbstract: Document assembly and other substantive legal practice applications are the most knowledgeintense 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.
Most legal tasks involve document preparation and review. Drafting effective texts is central to lawyering, judging, legislating, and regulating. How best to support that work with intelligent tools is an ancient topic in AI-and-Law research. For those tools to work, they must have good quality knowledge content to work with. Many alternative theories and techniques for modeling documents have been developed for particular kinds of situations. This article sketches a basic general theory of legal document modeling, with a focus on the key role of argumentation
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 (and assert) conclusions based on a balance of reasons that is not straightforwardly reducible to the application of rules. Recent AI & Law work has modeled reason-balancing, both within and across cases, with settheoretic and rule-or value-ordering approaches. This article explores how modeling it in 'choiceboxing' terms may yield new questions, insights, and tools.
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