In this paper, we examine the use of multi-lingual sentence embeddings to transfer predictive models for functional segmentation of adjudicatory decisions across jurisdictions, legal systems (common and civil law), languages, and domains (i.e. contexts). Mechanisms for utilizing linguistic resources outside of their original context have significant potential benefits in AI & Law because differences between legal systems, languages, or traditions often block wider adoption of research outcomes. We analyze the use of Language-Agnostic Sentence Representations in sequence labeling models using Gated Recurrent Units (GRUs) that are transferable across languages. To investigate transfer between different contexts we developed an annotation scheme for functional segmentation of adjudicatory decisions. We found that models generalize beyond the contexts on which they were trained (e.g., a model trained on administrative decisions from the US can be applied to criminal law decisions from Italy). Further, we found that training the models on multiple contexts increases robustness and improves overall performance when evaluating on previously unseen contexts. Finally, we found that pooling the training data from all the contexts enhances the models' in-context performance.

Access to justice could be significantly expanded if decision support systems were able to accurately interpret statements of fact by pro se (self-represented) litigants. Prior research, which has demonstrated that case decisions can often be predicted by machine-learning models trained on judges’ statements of facts, suggests the hypothesis that these same learning algorithms could be effectively applied to pro se litigants’ fact statements. However, there has been a dearth of corpora on which to test this hypothesis. This paper describes an experiment testing the ability to predict the outcome of pro se litigants’ complaints on a corpus of 5,842 cases initiated by citizen complaints. The results of this experiment were strikingly negative, suggesting that fact statements by unguided pro se litigants are far less amenable to simple machine-learning techniques than judges’ texts and appearing to disconfirm the hypothesis above.

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Legal decision-support systems have the potential to improve access to justice, administrative efficiency, and judicial consistency, but broad adoption of such systems is contingent on development of technologies with low knowledge-engineering, validation, and maintenance costs. This paper describes two approaches to an important form of legal decision support---explainable outcome prediction---that obviate both annotation of an entire decision corpus and manual processing of new cases. The first approach, which uses an Attention Network for prediction and attention weights to highlight salient case text, was shown to be capable of predicting decisions, but attention-weight-based text highlighting did not demonstrably improve human decision speed or accuracy in an evaluation with 61 human subjects. The second approach, termed SCALE (Semi-supervised Case Annotation for Legal Explanations), exploits structural and semantic regularities in case corpora to identify textual patterns that have both predictable relationships to case decisions and explanatory value.

This paper presents a novel approach to estimating healthcare fraud (HCF) risk that applies network algorithms to graphs derived from open source datasets. One group of algorithms calculates behavioral similarity to known fraudulent and non-fraudulent healthcare providers with respect to measurable healthcare activities, such as medical procedures and drug prescriptions. Another set of algorithms estimates propagation of risk from fraudulent healthcare providers through geospatial collocation, i.e., shared practice locations or other addresses. The algorithms were evaluated with respect to their ability to predict a provider's presence on the Office of the Inspector General's list of providers excluded from participation in Medicare and other Federal healthcare programs (exclusion). In an empirical evaluation, a combination of 11 features achieved an f-score of 0.919 and a ROC area of 0.960 in exclusion prediction. An ablation analysis showed that most of this predictive accuracy was the result of features that measure risk propagation through geospatial collocation.

This paper describes an algorithm, Distributed Pivot Clustering (DPC), that differs from prior distributed clustering algorithms in that it requires neither an inexpensive approximation of the actual distance function nor that pairs of elements in the same cluster share at least one exact feature value. Instead, DPC requires only that the distance function satisfy the triangle inequality and be of sufficiently high-granularity to permit the data to be partitioned into canopies of optimal size based on distance to reference elements, or pivots. An empirical evaluation demonstrated that DPC can lead to accurate distributed hierarchical agglomerative clustering provided that the triangle inequality and granularity requirements are met.

Incremental methods for detecting community structure are necessary when a graph's size or node-expansion cost makes global community-detection methods infeasible. Previous approaches to local community detection, which conflate edges between vertices in the immediate neighborhood of a partially-known community with edges to more distant vertices, often select vertices in an order that is suboptimal with respect to the actual community structure. This paper describes two new algorithms--MaxActivation and MaxDensity--whose vertex-selection policies focus on edges among the vertices in the partially-known community and its immediate neighborhood, ignoring edges to more distant vertices. In an empirical evaluation on a collection of natural and artificial graphs of varying degrees of community cohesion, the relative performance of alternative algorithms depended upon the degree distribution of each graph. These results demonstrate that the selection of an algorithm for incremental community detection should be guided by the characteristics of the graph to which it will be applied.

Name matching---recognizing when two different strings are likely to denote the same entity---is an important task in many legal information systems, such as case-management systems. The naming conventions peculiar to legal cases limit the effectiveness of generic approximate string-matching algorithms in this task. This paper proposes a three-stage framework for name matching, identifies how each stage in the framework addresses the naming variations that typically arise in legal cases, describes several alternative approaches to each stage, and evaluates the performance of various combinations of the alternatives on a representative collection of names drawn from a United States District Court case management system. The best tradeoff between accuracy and efficiency in this collection was achieved by algorithms that standardize capitalization, spacing, and punctuation; filter redundant terms; index using an abstraction function that is both order-insensitive and tolerant of small numbers of omissions or additions; and compare names in a symmetrical, word-by-word fashion.

Increasing numbers of litigants represent themselves in court. Advisory systems designed to help litigants understand the available legal remedies and satisfy the substantive and procedural requirements to obtain those remedies have the potential to assist these litigants and thereby reduce the burden that they impose on the courts. This paper presents a four-component model of advisory systems for prose litigants. This model was implemented in the Protection Order Advisory (POA), an advisory system for pro se Protection Order applicants, POA illustrates how existing inference, document-drafting, and interface-design techniques can be used to construct advisory systems for pro se litigants in a wide range of legal domains for which (1) determining whether a prima facie case is satisfied does not require open-textured reasoning, and (2) the documents required to initiate an action are characterized by homogeneity and simple structure.

The capacity for self-explanation can make computer-drafted documents more credible, assist in the retrieval and adaptation of archival documents, and permit comparison of documents at a deep level.We propose a knowledge-based model of documents that makes explicit the underlying goals that documents are intended to achieve and the stylistic conventions to which they must conform.These goals and conventions are expressed in a dual justification structure that represents the illocutionary and rhetorical dependencies underlying documents.After demonstrating how a document grammar derived from dual justification structures can be used to automate document drafting, we show how documents can exploit dual justification structures to "explain themselves" by answering queries about (1) the purposes for inclusion of text in the document and (2) the justification for propositions expressed in the text.This self-explanation framework has been implemented in the DOCU-PLANNER, a prototype document generation system that produces "queryable" documents.1 Introduction Legal document drafting is an essential professional skill for attorneys and judges.In the U.S., a significant portion of attorneys' workloads consists of drafting documents intended to precisely stipulate legal relationships such as wills, contracts, and leases, and persuasive documents arising from litigation such as pleadings, motions, and briefs.Document drafting can be viewed as a kind of configuration task in which textual elements are selected and arranged to satisfy the goals of the drafter and to conform to the stylistic conventions of the document genre.One source of complexity in document drafting is the combinatorics of selection and configuration decisions, which create large search spaces characteristic of most synthesis tasks.However, a more fundamental reason for the difficulty of document drafting is that the goals that documents are intended to achieve and the stylistic conventions to which they must conform are seldom made explicit.hmission to make digitabhard copy of all or part of this work for personal or chw~m use is granted without fee provided that the copies are not made or distributed for profit or commercial advantage.the copyright notice, the tide of Ihe p$!ication and its date appear.and notice is given that copying is by PWSSlOn of ACM, Inc.To copy otherwise.to republish, to post on servers or to redistribute to lists, requires prior specific pcmrission and/or fee.

This paper proposes a model of ration decidendi as a justification structure consisting of a series of reasoning steps, some of which relate abstract predicates to other abstract predicates and some of which relate abstract predicates to specific facts. This model satisfies four adequacy criteria for ratio decidendi identified from the jurisprudential literature. In particular, the model shows how the theory under which a case is decided controls its precedential effect. By contrast, a purely case-based model of ratio fails to account for the dependency of precedential effect on the theory of decision.

Article An issue-oriented approach to judicial document assembly Share on Author: L. Karl Branting View Profile Authors Info & Claims ICAIL '93: Proceedings of the 4th international conference on Artificial intelligence and lawAugust 1993 Pages 228–235https://doi.org/10.1145/158976.159005Online:01 August 1993Publication History 9citation190DownloadsMetricsTotal Citations9Total Downloads190Last 12 Months6Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Precedent-based legal reasoning depends on accurate assessment of relevant similarities between new cases and existing precedents. Determining the relevant similarities between a new case and a precedent with respect to a legal category requires knowing the explanation of the precedent's membership in the category. GREBE is a system that uses both general legal rules and specific explanations of precedents to evaluate legal predicates in new cases. GREBE assesses the similarity of a new case to a precedent of a legal category by attempting to find a pattern of relations in the new case that corresponds to the facts of the precedent responsible for its category membership. Missing relations in the new case are inferred by reusing other explanations from past cases.