Few automated legal reasoning systems have been developed in domains of law in which a judicial decision maker has extensive discretion in the exercise of his or her powers. Discretionary domains challenge existing artificial intelligence paradigms because models of judicial reasoning are difficult, if not impossible to specify. We argue that judicial discretion adds to the characterisation of law as open textured in a way which has not been addressed by artificial intelligence and law researchers in depth. We demonstrate that (...) systems for reasoning with this form of open texture can be built by integrating rule sets with neural networks trained with data collected from standard past cases. The obstacles to this approach include 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. A knowledge representation scheme based on the structure of arguments proposed by Toulmin has been used to overcome these obstacles. The system, known as Split Up, predicts judicial decisions in property proceedings within family law in Australia. Predictions from the system have been compared to those from a group of lawyers with favourable results. (shrink)

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.

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)

This paper investigates the importance of standardising and maintaining the transparency of advanced driver-assistance systems (ADAS) functions nomenclature, designs, and operations in all categories up until fully autonomous vehicles. The aim of this paper is to reveal the discrepancies in ADAS functions across automakers and discuss the underlying issues and potential solutions. In this pilot study, user manuals of various brands are reviewed systematically and critical analyses of common ADAS functions are conducted. The result shows that terminologies used to describe (...) ADAS functions vary widely across manufacturers and sometimes do not reflect their fundamental functions intuitively. Operational conditions and control procedures also vary across the selected models under this study. Due to this lack of consensus across the industry, drivers are not aware or well informed about ADAS functions in their vehicles, leading to a very low utilization rate and may lead to misuse of those functions. This paper provides insightful suggestions for the transport industry, Artificial Intelligence (AI) experts, and regulators to design frameworks and guidelines in governing the naming convention, operating conditions, control procedures, and information disclosure of ADAS. Such guidelines can be the foundations for regulating future AI-based self-driving functions. (shrink)

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)

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.

In attempting to build intelligent litigation support tools, we have moved beyond first generation, production rule legal expert systems. Our work integrates rule based and case based reasoning with intelligent information retrieval.When using the case based reasoning methodology, or in our case the specialisation of case based retrieval, we need to be aware of how to retrieve relevant experience. Our research, in the legal domain, specifies an approach to the retrieval problem which relies heavily on an extended object oriented/rule based (...) system architecture that is supplemented with causal background information. We use a distributed agent architecture to help support the reasoning process of lawyers. (shrink)

The paper sets out the challenges facing the Police in respect of the detection and prevention of the volume crime of burglary. A discussion of data mining and decision support technologies that have the potential to address these issues is undertaken and illustrated with reference the authors’ work with three Police Services. The focus is upon the use of “soft” forensic evidence which refers to modus operandi and the temporal and geographical features of the crime, rather than “hard” evidence such (...) as DNA or fingerprint evidence. Three objectives underpin this paper. First, given the continuing expansion of forensic computing and its role in the emergent discipline of Crime Science, it is timely to present a review of existing methodologies and research. Second, it is important to extract some practical lessons concerning the application of computer science within this forensic domain. Finally, from the lessons to date, a set of conclusions will be advanced, including the need for multidisciplinary input to guide further developments in the design of such systems. The objectives are achieved by first considering the task performed by the intended systems users. The discussion proceeds by identifying the portions of these tasks for which automation would be both beneficial and feasible. The knowledge discovery from databases process is then described, starting with an examination of the data that police collect and the reasons for storing it. The discussion progresses to the development of crime matching and predictive knowledge which are operationalised in decision support software. The paper concludes by arguing that computer science technologies which can support criminal investigations are wide ranging and include geographical information systems displays, clustering and link analysis algorithms and the more complex use of data mining technology for profiling crimes or offenders and matching and predicting crimes. We also argue that knowledge from disciplines such as forensic psychology, criminology and statistics are essential to the efficient design of operationally valid systems. (shrink)