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 focus.In 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 (...) systems.We 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. (shrink)

The first issue of Artificial Intelligence and Law journal was published in 1992. This paper offers some commentaries on papers drawn from the Journal’s third decade. They indicate a major shift within Artificial Intelligence, both generally and in AI and Law: away from symbolic techniques to those based on Machine Learning approaches, especially those based on Natural Language texts rather than feature sets. Eight papers are discussed: two concern the management and use of documents available on the World Wide Web, (...) and six apply machine learning techniques to a variety of legal applications. (shrink)

Information technology is so ubiquitous and AI’s progress so inspiring that also legal professionals experience its benefits and have high expectations. At the same time, the powers of AI have been rising so strongly that it is no longer obvious that AI applications (whether in the law or elsewhere) help promoting a good society; in fact they are sometimes harmful. Hence many argue that safeguards are needed for AI to be trustworthy, social, responsible, humane, ethical. In short: AI should be (...) good for us. But how to establish proper safeguards for AI? One strong answer readily available is: consider the problems and solutions studied in AI & Law. AI & Law has worked on the design of social, explainable, responsible AI aligned with human values for decades already, AI & Law addresses the hardest problems across the breadth of AI (in reasoning, knowledge, learning and language), and AI & Law inspires new solutions (argumentation, schemes and norms, rules and cases, interpretation). It is argued that the study of AI as Law supports the development of an AI that is good for us, making AI & Law more relevant than ever. (shrink)

This paper considers the problem of how to evaluate an offender’s criminal record. This evaluation is part of the sentencing process carried out by a judge, and may be complicated in the case of offenders with a heavy record. We give a comprehensive overview of the approach to an offender’s past record in various (Western) countries, considering the two major approaches: desert-based and utilitarian. The paper describes the determination of the parameters involved in the evaluation, and the construction of a (...) decision support system to be used by a judge about to pass sentence in a criminal case. The system may be used as a stand-alone system, but can also be integrated as a component of a general sentencing support system, e.g. a statistical sentencing information system. The system is a knowledge-based system. The knowledge base (in rule-form) was created primarily by eliciting knowledge from experts (judges, lawyers, academics and probation officers), but also by applying statutory law, case law and legal authoritative texts. It is flexible in the sense, that a user can introduce his1 own preferences (expressed as numerical coefficients) and thereby apply different sentencing principles. The prototype is at present undergoing testing by some of the participating judges. (shrink)

We present a reference model for finding evidence of discrimination in datasets of historical decision records in socially sensitive tasks, including access to credit, mortgage, insurance, labor market and other benefits. We formalize the process of direct and indirect discrimination discovery in a rule-based framework, by modelling protected-by-law groups, such as minorities or disadvantaged segments, and contexts where discrimination occurs. Classification rules, extracted from the historical records, allow for unveiling contexts of unlawful discrimination, where the degree of burden over protected-by-law (...) groups is evaluated by formalizing existing norms and regulations in terms of quantitative measures. The measures are defined as functions of the contingency table of a classification rule, and their statistical significance is assessed, relying on a large body of statistical inference methods for proportions. Key legal concepts and reasonings are then used to drive the analysis on the set of classification rules, with the aim of discovering patterns of discrimination, either direct or indirect. Analyses of affirmative action, favoritism and argumentation against discrimination allegations are also modelled in the proposed framework. Finally, we present an implementation, called LP2DD, of the overall reference model that integrates induction, through data mining classification rule extraction, and deduction, through a computational logic implementation of the analytical tools. The LP2DD system is put at work on the analysis of a dataset of credit decision records. (shrink)

Much legal research focuses on understanding how judicial decision-makers exercise their discretion. In this paper we examine the notion of legal or judicial discretion, and weaker and stronger forms of discretion. At all times our goal is to build cognitive models of the exercise of discretion, with a view to building computer software to model and primarily support decision-making. We observe that discretionary decision-making can best be modeled using three independent axes: bounded and unbounded, defined and undefined, and binary and (...) continuous. Examples of legal tasks are given from each of the eight ensuing octants and we conclude by saying what this model shows about current legal trends. We should stress that our taxonomy has been based on our observations of how discretionary legal decisions are made. No claim is made that our model is either complete (providing advice in every domain) or exact, but it does help knowledge engineers construct legal decision support systems in discretionary domains. (shrink)

In this article a dialectical model for practical reasoning within a community, based on the Generic/Actual Argument Model (GAAM) is advanced and its application to deliberative dialogue discussed. The GAAM, offers a dynamic template for structuring knowledge within a domain of discourse that is connected to and regulated by a community. The paper demonstrates how the community accepted generic argument structure acts to normatively influence both admissible reasoning and the progression of dialectical reasoning between participants. It is further demonstrated that (...) these types of deliberation dialogues supported by the GAAM comply with criteria for normative principles for deliberation, specifically, Alexy’s rules for discourse ethics and Hitchcock’s Principles of Rational Mutual Inquiry. The connection of reasoning to the community in a documented and transparent structure assists in providing best justified reasons, principles of deliberation and ethical discourse which are important advantages for reasoning communities. (shrink)

The first issue of _Artificial Intelligence and Law_ journal was published in 1992. This paper provides commentaries on landmark papers from the first decade of that journal. The topics discussed include reasoning with cases, argumentation, normative reasoning, dialogue, representing legal knowledge and neural networks.

The first issue of _Artificial Intelligence and Law_ journal was published in 1992. This special issue marks the 30th anniversary of the journal by reviewing the progress of the field through thirty commentaries on landmark papers and groups of papers from that journal.

Negotiation Support Systems have traditionally modelled the process of negotiation. They often rely on mathematical optimisation techniques and ignore heuristics and other methods derived from practice. Our goal is to develop systems capable of decision support to help resolve a given dispute. A system we have constructed, Family_Winner, uses empirical evidence to dynamically modify initial preferences throughout the negotiation process. It sequentially allocates issues using trade-offs and compensation opportunities inherent in the dispute.

A number of increasingly sophisticated technologies are now being used to support complex decision-making in a range of contexts. This paper reports on work undertaken to provide decision support in the discretionary domain of sentencing by referring to a recently created Toulmin argument based model that involves the interplay and weighting of relevant rule-based and discretionary factors used in a decisional process. Judicial discretion, particularly in the sentencing phase, is one of the mainstays of justice systems that favour individualised justice. (...) The study discusses the modelling process in Victorian courts in Australia, where the handing down of an appropriate custodial or non-custodial sentence requires the consideration of many factors. Tools and techniques used to capture relevant expert knowledge and display it both as a paper model and as an online prototype application are discussed. (shrink)

Our goal is to model reasoning in discretionary legal domains. To do so, we use Knowledge Discovery from Database Techniques. However there are obstacles to this approach, including difficulties in generating explanations once conclusions have been inferred, difficulties associated with the collection of sufficient data from past cases and difficulties associated with integrating two vastly different paradigms. Toulmin’s treatise on the uses of argument can be gainfully employed to construct legal decision support systems in discretionary domains. We show how we (...) can use Toulmin’s approach to build such systems with examples taken from the domains of eligibility for legal aid, evaluation of eyewitness evidence, family law, refugee law and sentencing. We then show how Toulmin Argument Structures can be developed to construct an Online Dispute Resolution environment that allows for determining BATNAs, exchanging opinions and providing advice about tradeoffs. (shrink)