64 resultados para Wheeler, Ephraim.


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Lennart Åqvist (1992) proposed a logical theory of legal evidence, based on the Bolding-Ekelöf of degrees of evidential strength. This paper reformulates Åqvist's model in terms of the probabilistic version of the kappa calculus. Proving its acceptability in the legal context is beyond the present scope, but the epistemological debate about Bayesian Law isclearly relevant. While the present model is a possible link to that lineof inquiry, we offer some considerations about the broader picture of thepotential of AI & Law in the evidentiary context. Whereas probabilisticreasoning is well-researched in AI, calculations about the threshold ofpersuasion in litigation, whatever their value, are just the tip of theiceberg. The bulk of the modeling desiderata is arguably elsewhere, if one isto ideally make the most of AI's distinctive contribution as envisaged forlegal evidence research.

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Review of: Handbook of Psychology in Legal Contexts. Ray Bull and David Carson (eds.) Wiley-Blackwell. 1999.

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This Acknowledgement refers to the special issue "Formal Approaches to Legal Evidence" of the Artificial Intelligence and Law, September 2001, Vol. 9, Issue 2-3, which was guest edited by Ephraim Nissan.

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This special issue "Formal Approaches to Legal Evidence" of the Artificial Intelligence and Law, September 2001, Vol. 9, Issue 2-3, which was guest edited by Ephraim Nissan.

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For the purposes of starting to tackle, within artificial intelligence (AI), the narrative aspects of legal narratives in a criminal evidence perspective, traditional AI models of narrative understanding can arguably supplement extant models of legal narratives from the scholarly literature of law, jury studies, or the semiotics of law. Not only: the literary (or cinematic) models prominent in a given culture impinge, with their poetic conventions, on the way members of the culture make sense of the world. This shows glaringly in the sample narrative from the Continent-the Jama murder, the inquiry, and the public outcry-we analyse in this paper. Apparently in the same racist crime category as the case of Stephen Lawrence's murder (in Greenwich on 22 April 1993) with the ensuing still current controversy in the UK, the Jama case (some 20 years ago) stood apart because of a very unusual element: the eyewitnesses identifying the suspects were a group of football referees and linesmen eating together at a restaurant, and seeing the sleeping man as he was set ablaze in a public park nearby. Professional background as witnesses-cum-factfinders in a mass sport, and public perceptions of their required characteristics, couldn't but feature prominently in the public perception of the case, even more so as the suspects were released by the magistrate conducting the inquiry. There are sides to this case that involve different expected effects in an inquisitorial criminal procedure system from the Continent, where an investigating magistrate leads the inquiry and prepares the prosecution case, as opposed to trial by jury under the Anglo-American adversarial system. In the JAMA prototype, we tried to approach the given case from the coign of vantage of narrative models from AI.

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In judicial decision making, the doctrine of chances takes explicitly into account the odds. There is more to forensic statistics, as well as various probabilistic approaches, which taken together form the object of an enduring controversy in the scholarship of legal evidence. In this paper, I reconsider the circumstances of the Jama murder and inquiry (dealt with in Part I of this paper: 'The JAMA Model and Narrative Interpretation Patterns'), to illustrate yet another kind of probability or improbability. What is improbable about the Jama story is actually a given, which contributes in terms of dramatic underlining. In literary theory, concepts of narratives being probable or improbable date back from the eighteenth century, when both prescientific and scientific probability were infiltrating several domains, including law. An understanding of such a backdrop throughout the history of ideas is, I claim, necessary for Artificial Intelligence (AI) researchers who may be tempted to apply statistical methods to legal evidence. The debate for or against probability (and especially Bayesian probability) in accounts of evidence has been flourishing among legal scholars; nowadays both the Bayesians (e.g. Peter Tillers) and the Bayesio-skeptics (e.g. Ron Allen), among those legal scholars who are involved in the controversy, are willing to give AI research a chance to prove itself and strive towards models of plausibility that would go beyond probability as narrowly meant. This debate within law, in turn, has illustrious precedents: take Voltaire, he was critical of the application of probability even to litigation in civil cases; take Boole, he was a starry-eyed believer in probability applications to judicial decision making. Not unlike Boole, the founding father of computing, nowadays computer scientists approaching the field may happen to do so without full awareness of the pitfalls. Hence, the usefulness of the conceptual landscape I sketch here.

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Fictitious personal names and toponyms are not infrequent in legal casenotes as used for didactic purposes nowadays. There is a long tradition of fictitious names being used in the legal literature. The problem with medieval or early modern legal (here, rabbinical) responsa is that if they are used as evidence for historical purposes, as though they were chronicles, confusion may occurs. Historian Eliezer Bashan showed that this is the case, indeed, with particular reference to rabbinical responsa from the Ottoman empire where Holy Land toponyms occur. He set forth several tentative rules to decide whether a toponym is there to literally refer to the place it names, or whether, instead, the name is used fictitiously. This paper formalizes the ruleset.

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Daedalus is a computer tool, developed by an Italian magistrate - Carmelo Asaro - and integrated in his own daily routine as an investigating magistrate conducting inquiries, then as a prosecutor if and when the case investigated goes to court. This tool has recently been adopted by magistrates in judiciary offices throughout Italy, spawning moreover other related projects. First, this paper describes a sample session with daedalus. Next, an overview of an array of judicial tools leads to positioning daedalus in the context of the spectrum.

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A formal representation is given of the situational structure, and the agents' beliefs about personal identity, in the Smemorato di Collegno amnesia case tried in 1927, in Pollenza, Italy. Another section discusses and formalizes a sample heuristic rule for conjecturing whether an individual identity other than personal, being conveyed by a toponym, was used literally or fictitiously in a given historical corpus of legal casenotes. For example, a landlocked city being named and referred to as though it was a sea port is a fairly good cue for assuming that the toponym is a disguise. Yet, the interpretation is governed by other conventions, when in a play by Shakeaspeare it is stated that a given scene is set on the sea coast of Bohemia. Further discussion of a situational casuistry for identification (especially individual and personal) along with more formal representations will appear in a companion paper "nissanidentifpirandello", also at the disciplinary meet of AI formalisms and legal applications.

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This paper describes modeling technology and its use in providing data governing the assembly and subsequent reliability of electronic chip components on printed circuit boards (PCBs). Products, such as mobile phones, camcorders, intelligent displays, etc., are changing at a tremendous rate where newer technologies are being applied to satisfy the demands for smaller products with increased functionality. At ever decreasing dimensions, and increasing number of input/output connections, the design of these components, in terms of dimensions and materials used, is playing a key role in determining the reliability of the final assembly. Multiphysics modeling techniques are being adopted to predict a range of interacting physics-based phenomena associated with the manufacturing process. For example, heat transfer, solidification, marangoni fluid flow, void movement, and thermal-stress. The modeling techniques used are based on finite volume methods that are conservative and take advantage of being able to represent the physical domain using an unstructured mesh. These techniques are also used to provide data on thermal induced fatigue which is then mapped into product lifetime predictions.