Marxist-lessigism

Computer users of the world have united behind Stanford law professor Lawrence Lessig—and what they're doing is much more important than his critics realize.

ICANN and the concept of democratic deficit

The Internet Corporation for Assigned Names and Numbers (ICANN) is an institution besieged. It has endeavored to be democratic but its attempts to do so have been disastrous. The typical explanation for this is that the problem is with ICANN: it fails to meet its democratic obligations. My view is that the problem is with our understanding of "democracy." Democracy is an empty concept that fails to describe few, if any, of our genuine political commitments. In the real world, the failings inherent in "democracy" have been papered over by some unusual characteristics of the physical political process. However, in online trans-national institutions like ICANN, democracy is exposed as a poor substitute for a number of other conceptions of our political commitments. This Article seeks to articulate these political commitments and to explain why democracy and ICANN are such a poor mix. It begins by charting the rise of ICANN and its attempts to be democratic. It then explains why democracy is an empty shell of a concept. It then explores some features of democracy and ICANN, explaining why the online world exposes limitations in implications of democracy such as the nature of the demos, the idea of constituencies, direct democracy, voting, and the like. It concludes that ICANN's example demonstrates that democracy is in fact anything but a coherent general theory of political action. We need to consider, then, whether we should continue to berate ICANN for its undemocratic actions.

Reason is too large : analogy and precedent in law

Hunter argues that cognitive science models of human thinking explain how analogical reasoning and precedential reasoning operate in law. He offers an explanation of why various legal theories are so limited and calls for greater attention to what is actually happening when lawyers and judges reason, by analogy, with precedent.

Don't think but look : a Gestalt interactionist approach to legal thinking

We propose here a new approach to legal thinking that is based on principles of Gestalt perception. Using a Gestalt view of perception, which sees perception as the process of building a conceptual representation of the given stimulus, we articulate legal thinking as the process of building a representation for the given facts of a case. We propose a model in which top-down and bottom-up processes interact together to build arguments (or representations) in legal thinking. We discuss some implications of our approach, especially with respect to modeling precedential reasoning and creativity in legal thinking.

Out of their minds: legal theory in neural networks

This paper examines the use of connectionism (neural networks) in modelling legal reasoning. I discuss how the implementations of neural networks have failed to account for legal theoretical perspectives on adjudication. I criticise the use of neural networks in law, not because connectionism is inherently unsuitable in law, but rather because it has been done so poorly to date. The paper reviews a number of legal theories which provide a grounding for the use of neural networks in law. It then examines some implementations undertaken in law and criticises their legal theoretical naïvete. It then presents a lessons from the implementations which researchers must bear in mind if they wish to build neural networks which are justified by legal theories.

The IKBALS project: multi-modal reasoning in legal knowledge based systems

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. Our approach to integrating rule based reasoning, case based reasoning and case based retrieval is contrasted to the CABARET and PROLEXS architectures which rely on a centralised blackboard architecture. We discuss in detail how our various cooperating agents interact, and provide examples of the system at work. The IKBALS system uses a specialised induction algorithm to induce rules from cases. These rules are then used as indices during the case based retrieval process. Because we aim to build legal support tools which can be modified to suit various domains rather than single purpose legal expert systems, we focus on principles behind developing legal knowledge based systems. The original domain chosen was theAccident Compensation Act 1989 (Victoria, Australia), which relates to the provision of benefits for employees injured at work. For various reasons, which are indicated in the paper, we changed our domain to that ofCredit Act 1984 (Victoria, Australia). This Act regulates the provision of loans by financial institutions. The rule based part of our system which provides advice on the Credit Act has been commercially developed in conjunction with a legal firm. We indicate how this work has lead to the development of a methodology for constructing rule based legal knowledge based systems. We explain the process of integrating this existing commercial rule based system with the case base reasoning and retrieval architecture.

Reasoning paradigms in legal decision support systems

In this paper we discuss the strengths and weaknesses of a range of artificial intelligence approaches used in legal domains. Symbolic reasoning systems which rely on deductive, inductive and analogical reasoning are described and reviewed. The role of statistical reasoning in law is examined, and the use of neural networks analysed. There is discussion of architectures for, and examples of, systems which combine a number of these reasoning strategies. We conclude that to build intelligent legal decision support systems requires a range of reasoning strategies.

'Look and feel' computer copyright in the United States and Australia (part 2)

Australian law similar to that of United States -- Australian law requires copyright must subsist in plaintiff's material and defendent's work must infringe plaintiff's copyright to find defendent liable for illegal copying -- subsistence -- infringement -- two cases that touch on 'look and feel' issue -- passing-off -- look and feel of computer program deserves protection

Near knowledge : inductive learning systems in law

Induction is an interesting model of legal reasoning, since it provides a method of capturing initial states of legal principles and rules, and adjusting these principles and rules over time as the law changes. In this article I explain how Artificial Intelligence-based inductive learning algorithms work, and show how they have been used in law to model legal domains. I identify some problems with implementations undertaken in law to date, and create a taxonomy of appropriate cases to use in legal inductive inferencing systems. I suggest that inductive learning algorithms have potential in modeling law, but that the artificial intelligence implementations to date are problematic. I argue that induction should be further investigated, since it has the potential to be an extremely useful mechanism for understanding legal domains.