Modelling causality in law = Modélisation de la causalité en droit

L'intérêt en apprentissage machine pour étudier la causalité s'est considérablement accru ces dernières années. Cette approche est cependant encore peu répandue dans le domaine de l’intelligence artificielle (IA) et du droit. Elle devrait l'être. L'approche associative actuelle d’apprentissage machine révèle certaines limites que l'analyse causale peut surmonter. Cette thèse vise à découvrir si les modèles causaux peuvent être utilisés en IA et droit. Nous procédons à une brève revue sur le raisonnement et la causalité en science et en droit. Traditionnellement, les cadres normatifs du raisonnement étaient la logique et la rationalité, mais la théorie duale démontre que la prise de décision humaine dépend de nombreux facteurs qui défient la rationalité. À ce titre, des statistiques et des probabilités étaient nécessaires pour améliorer la prédiction des résultats décisionnels. En droit, les cadres de causalité ont été définis par des décisions historiques, mais la plupart des modèles d’aujourd’hui de l'IA et droit n'impliquent pas d'analyse causale. Nous fournissons un bref résumé de ces modèles, puis appliquons le langage structurel de Judea Pearl et les définitions Halpern-Pearl de la causalité pour modéliser quelques décisions juridiques canadiennes qui impliquent la causalité. Les résultats suggèrent qu'il est non seulement possible d'utiliser des modèles de causalité formels pour décrire les décisions juridiques, mais également utile car un schéma uniforme élimine l'ambiguïté. De plus, les cadres de causalité sont utiles pour promouvoir la responsabilisation et minimiser les biais.The machine learning community’s interest in causality has significantly increased in recent years. This trend has not yet been made popular in AI & Law. It should be because the current associative ML approach reveals certain limitations that causal analysis may overcome. This research paper aims to discover whether formal causal frameworks can be used in AI & Law. We proceed with a brief account of scholarship on reasoning and causality in science and in law. Traditionally, normative frameworks for reasoning have been logic and rationality, but the dual theory has shown that human decision-making depends on many factors that defy rationality. As such, statistics and probability were called for to improve the prediction of decisional outcomes. In law, causal frameworks have been defined by landmark decisions but most of the AI & Law models today do not involve causal analysis. We provide a brief summary of these models and then attempt to apply Judea Pearl’s structural language and the Halpern-Pearl definitions of actual causality to model a few Canadian legal decisions that involve causality. Results suggest that it is not only possible to use formal causal models to describe legal decisions, but also useful because a uniform schema eliminates ambiguity. Also, causal frameworks are helpful in promoting accountability and minimizing biases

Legal Knowledge and Information Systems - JURIX 2017: The Thirtieth Annual Conference

The proceedings of the 30th International Conference on Legal Knowledge and Information Systems – JURIX 2017. For three decades, the JURIX conferences have been held under the auspices of the Dutch Foundation for Legal Knowledge Based Systems (www.jurix.nl). In the time, it has become a European conference in terms of the diverse venues throughout Europe and the nationalities of participants

Abduction, Dispositions, and Alternatives in Science : On the Rational Reconstruction of Scientific Negotiation

Abduction, Dispositions, and Alternatives in Science : On the Rational Reconstruction of Scientific Negotiatio

Using Norms To Control Open Multi-Agent Systems

Internet es, tal vez, el avance científico más relevante de nuestros días. Entre otras cosas, Internet ha permitido la evolución de los paradigmas de computación tradicionales hacia el paradigma de computaciónn distribuida, que se caracteriza por utilizar una red abierta de ordenadores. Los sistemas multiagente (SMA) son una tecnolog a adecuada para abordar los retos motivados por estos sistemas abiertos distribuidos. Los SMA son aplicaciones formadas por agentes heterog eneos y aut onomos que pueden haber sido dise~nados de forma independiente de acuerdo con objetivos y motivaciones diferentes. Por lo tanto, no es posible realizar ninguna hip otesis a priori sobre el comportamiento de los agentes. Por este motivo, los SMA necesitan de mecanismos de coordinaci on y cooperaci on, como las normas, para garantizar el orden social y evitar la aparici on de conictos. El t ermino norma cubre dos dimensiones diferentes: i) las normas como un instrumento que gu a a los ciudadanos a la hora de realizar acciones y actividades, por lo que las normas de nen los procedimientos y/o los protocolos que se deben seguir en una situaci on concreta, y ii) las normas como ordenes o prohibiciones respaldadas por un sistema de sanciones, por lo que las normas son medios para prevenir o castigar ciertas acciones. En el area de los SMA, las normas se vienen utilizando como una especi caci on formal de lo que est a permitido, obligado y prohibido dentro de una sociedad. De este modo, las normas permiten regular la vida de los agentes software y las interacciones entre ellos. La motivaci on principal de esta tesis es permitir a los dise~nadores de los SMA utilizar normas como un mecanismo para controlar y coordinar SMA abiertos. Nuestro objetivo es elaborar mecanismos normativos a dos niveles: a nivel de agente y a nivel de infraestructura. Por lo tanto, en esta tesis se aborda primero el problema de la de nici on de agentes normativos aut onomos que sean capaces de deliberar acercaCriado Pacheco, N. (2012). Using Norms To Control Open Multi-Agent Systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17800Palanci

Rationality and the Structure of the Self, Volume I: The Humean Conception

The Humean conception of the self consists in the belief-desire model of motivation and the utility-maximizing model of rationality. This conception has dominated Western thought in philosophy and the social sciences ever since Hobbes’ initial formulation in Leviathan and Hume’s elaboration in the Treatise of Human Nature. Bentham, Freud, Ramsey, Skinner, Allais, von Neumann and Morgenstern and others have added further refinements that have brought it to a high degree of formal sophistication. Late twentieth century moral philosophers such as Rawls, Brandt, Frankfurt, Nagel and Williams have taken it for granted, and have made use of it to supply metaethical foundations for a wide variety of normative moral theories. But the Humean conception of the self also leads to seemingly insoluble problems about moral motivation, rational final ends, and moral justification. Can it be made to work

Theme Aspect Argumentation Model for Handling Fallacies

From daily discussions to marketing ads to political statements, information manipulation is rife. It is increasingly more important that we have the right set of tools to defend ourselves from manipulative rhetoric, or fallacies. Suitable techniques to automatically identify fallacies are being investigated in natural language processing research. However, a fallacy in one context may not be a fallacy in another context, so there is also a need to explain how and why it has come to be judged a fallacy. For the explainable fallacy identification, we present a novel approach to characterising fallacies through formal constraints, as a viable alternative to more traditional fallacy classifications by informal criteria. To achieve this objective, we introduce a novel context-aware argumentation model, the theme aspect argumentation model, which can do both: the modelling of a given argumentation as it is expressed (rhetorical modelling); and a deeper semantic analysis of the rhetorical argumentation model. By identifying fallacies with formal constraints, it becomes possible to tell whether a fallacy lurks in the modelled rhetoric with a formal rigour. We present core formal constraints for the theme aspect argumentation model and then more formal constraints that improve its fallacy identification capability. We show and prove the consequences of these formal constraints. We then analyse the computational complexities of deciding the satisfiability of the constraints