Artificial intelligence and distance learning philosophy in support of PfP mandate

Computers have long been utilised in the legal environment. The main use of computers however, has merely been to automate office tasks. More exciting is the prospect of using artificial intelligence (AI) technology to create computers that can emulate the substantive legal jobs performed by lawyers, to create computers that can autonomously reason with the law to determine legal solutions, for example: structuring and support of Partnership for Peace (PfP) mandate. Such attempts have not been successful jet. Modelling the law and emulating the processes of legal reasoning have proved to be more complex and subtle than originally envisaged. The adoption by AI researchers specialising in law of new AI techniques, such as case based reasoning, neural networks, fuzzy logic, deontic logics and non-monotonic logics, may move closer to achieving an automation of legal reasoning. Unfortunately these approaches also suffer several drawbacks that will need to be overcome if this is to be achieved. Even if these new techniques do not achieve an automation of legal reasoning however, they will still be valuable in better automating office tasks and in providing insights about the nature of law. An idea to apply the technology of intelligent multi-agent systems to the computer aided learning (CAL) in law, is currently being developed as a research project by the author of this article (see e.g. [Antoliš, 2002.]). Similar projects are usually based on the most modern technologies of multimedia and hypermedia, as it was implemented in this article. The theoretical foundations of the design and architecture of intelligent system for decision support process in law and distance-learning environment are, however, at their early stage of development

A formal analysis of some factor- and precedent-based accounts of precedential constraint

In this paper several recent factor- and dimension-based models of precedential constraint are formally investigated and an alternative dimension-based model is proposed. Simple factor- and dimension-based syntactic criteria are identified for checking whether a decision in a new case is forced, in terms of the relevant differences between a precedent and a new case, and the difference between absence of factors and negated factors in factor-based models is investigated. Then Horty’s and Rigoni’s recent dimension-based models of precedential constraint are critically examined. An alternative to their reason models is proposed which is less expressive but arguably easier to apply in practice

Creating New Pathways to Justice Using Simple Artificial Intelligence and Online Dispute Resolution

Access to justice in can be improved significantly through implementation of simple artificial intelligence (AI) based expert systems deployed within a broader online dispute resolution (ODR) framework. Simple expert systems can bridge the ‘implementation gap’ that continues to impede the adoption of AI in the justice domain. This gap can be narrowed further through the design of multi-disciplinary expert systems that address user needs through simple, non-legalistic user interfaces. This article provides a non-technical conceptual description of an expert system designed to enhance access to justice for non-experts. The system’s knowledge base would be populated with expert knowledge from the justice and dispute resolution domains. A conditional logic rule-based system forms the basis of the inference engine located between the knowledge base and a questionnaire-based user interface. The expert system’s functions include problem diagnosis, delivery of customized information, self-help support, triage and streaming into subsequent ODR processes. Its usability is optimized through the engagement of human computer interaction (HCI) and effective computing techniques that engage the social and emotional sides of technology. The conceptual descriptions offered in this article draw support from empirical observations of an innovative project aimed at creating an expert system for an ODR-enabled civil justice tribunal

Creating New Pathways to Justice Using Simple Artificial Intelligence and Online Dispute Resolution

Access to justice in can be improved significantly through implementation of simple artificial intelligence (AI) based expert systems deployed within a broader online dispute resolution (ODR) framework. Simple expert systems can bridge the ‘implementation gap’ that continues to impede the adoption of AI in the justice domain. This gap can be narrowed further through the design of multi-disciplinary expert systems that address user needs through simple, non-legalistic user interfaces. This article provides a non-technical conceptual description of an expert system designed to enhance access to justice for non-experts. The system’s knowledge base would be populated with expert knowledge from the justice and dispute resolution domains. A conditional logic rule-based system forms the basis of the inference engine located between the knowledge base and a questionnaire-based user interface. The expert system’s functions include problem diagnosis, delivery of customized information, self-help support, triage and streaming into subsequent ODR processes. Its usability is optimized through the engagement of human computer interaction (HCI) and effective computing techniques that engage the social and emotional sides of technology. The conceptual descriptions offered in this article draw support from empirical observations of an innovative project aimed at creating an expert system for an ODR-enabled civil justice tribunal

Changing hearts and minds in Mexico: a cognitive-jurisprudential approach to legal education reform in a legal system in transition

The starting assumption of this thesis is that to fully understand legal practices – including legal reasoning – we need to get a grasp of the complex body of knowledge into which they are immersed. Legal studies have often assumed that legal knowledge can be reduced to the knowledge of legal rules. This research departs from this perspective and argues for an understanding of legal knowledge that includes the complex set of conceptual, procedural and affective considerations which shape legal practices in general, and legal reasoning in particular. Herein we argue that not only legal knowledge is wider than the knowledge of rules, but that there are also some aspects of legal practice that cannot be properly addressed by explicitly drafted legal rules. We purport to build such an account upon epistemologically-informed comparative legal perspectives and insights of the cognitive sciences, by way of discussing a particular factual problem. The case to be studied in this thesis is the apparent loss of certainty in Mexican legal practice, when legal professionals engage in precedent-based reasoning. The situation, which was first reported in 2006, has remained broadly unexplored, and by default has been reputed as a problem concerning the set of explicit rules that regulate the system of legal precedents in that national context. We argue that the situation cannot be fully comprehended and remedied if we exclusively focus on the dimension of legal rules, but that it would be better understood if we direct our attention to the deeper knowledge structures in which that practice is immersed. This thesis builds a case for a broadened approach to legal knowledge by unveiling the historically built knowledge structures in which the Mexican understanding of precedents is embedded. As we shall see, this particular framework has acted as a deterrent to precedent-based reasoning, as accounted by a set of theories of law and legal reasoning. By focusing on the several processes of legal change and the collateral epistemic revisions that Mexican legal professionals seem to be experiencing for the past decades, this thesis argues that changing deeply embedded knowledge structures is a difficult task that needs to be supported by revising the processes of knowledge construction, and most importantly legal education