The Richness of Contract Theory

This essay is a review of The Richness of Contract Law: An Analysis and Critique of Conemporary Theories of Contract Law by Robert A. Hillman (1997). Throughout the book, Hillman offers a number of useful insights about various issues of contract law and theory--as he has in his numerous law review articles--but in this review the author is concerned with his overall theme: a general skepticism about unifying or highly abstract contract theories that fail to mirror the richness of contract law. In this regard, Hillman stands in the realist tradition of the previous generation of contracts scholars. Hillman attempts to justify this stance by examining a number of doctrinal contexts: contract formation, unconscionability, and good faith. Hillman considers a variety of theoretical approaches: promise theorists, reliance theorists, feminist theorists, efficiency theorists, relational theorists, and critical legal scholars

Three Stories and Their Morals

Fundamentally, the common law tradition is a collection of stories. Stories also become the law professor\u27s stock in trade. We tell students stories or have them read stories in the form of cases or hypothetical situations and help them discern the morals to the stories-i.e., what the stories mean in the context of business or in their business lives? In a sense, that is what the Socratic Method is all about: analyzing stories in the form of cases and discerning their greater meaning. In this paper I will relate three true stories within the context of just-in-time production management and develop their morals or implications for business and business lawyers

Participatory Lawyering & The Ivory Tower: Conducting a Forensic Law Audit in the Aftermath of Virginia Tech

The tragic events at Virginia Tech in 2007 sent a cold wind blowing through the halls of higher education institutions: a Virginia Tech student, who had fallen through the cracks of the school\u27s mental health services and disciplinary procedures, armed himself with firearms and murdered thirty-two students and a professor before committing suicide. In the wake of that massacre, several states and individual interest groups issued reports on campus readiness for similar catastrophes. A consistent theme throughout those reports emphasized the necessity for individual institutions to review their procedures to deal with campus violence. This Article focuses on that institutional review and the role of lawyers in assisting colleges and universities in formulating better and more comprehensive procedures for preventing campus violence in general, but with an emphasis on preventing similar catastrophes, or at worst, minimizing their devastation. The lawyer has the best opportunity to assist by participating in the process rather than either dictating its conduct or reviewing the product after the fact. Preventive lawyering and collaborating with the academy are the only successful means for adequately addressing comprehensive plans that manage the risks raised by the needs of the new consumer student and that create a campus culture that does not tolerate campus violence. Specifically, this Article summarizes how the lawyer\u27s collaboration with the academy should neatly incorporate the academic ends of the institution with legal ends that could minimize both the harm and the costs of campus violence

Towards a legal definition of machine intelligence: the argument for artificial personhood in the age of deep learning.

The paper dissects the intricacies of Automated Decision Making (ADM) and urges for refining the current legal definition of AI when pinpointing the role of algorithms in the advent of ubiquitous computing, data analytics and deep learning. ADM relies upon a plethora of algorithmic approaches and has already found a wide range of applications in marketing automation, social networks, computational neuroscience, robotics, and other fields. Our main aim here is to explain how a thorough understanding of the layers of ADM could be a first good step towards this direction: AI operates on a formula based on several degrees of automation employed in the interaction between the programmer, the user, and the algorithm; this can take various shapes and thus yield different answers to key issues regarding agency. The paper offers a fresh look at the concept of "Machine Intelligence", which exposes certain vulnerabilities in its current legal interpretation. Most importantly, it further helps us to explore whether the argument for "artificial personhood" holds any water. To highlight this argument, analysis proceeds in two parts: Part 1 strives to provide a taxonomy of the various levels of automation that reflects distinct degrees of Human - Machine interaction and can thus serve as a point of reference for outlining distinct rights and obligations of the programmer and the consumer: driverless cars are used as a case study to explore the several layers of human and machine interaction. These different degrees of automation reflect various levels of complexities in the underlying algorithms, and pose very interesting questions in terms of agency and dynamic tasks carried out by software agents. Part 2 further discusses the intricate nature of the underlying algorithms and artificial neural networks (ANN) that implement them and considers how one can interpret and utilize observed patterns in acquired data. Is "artificial personhood" a sufficient legal response to highly sophisticated machine learning techniques employed in decision making that successfully emulate or even enhance human cognitive abilities

A study into the layers of automated decision-making: emergent normative and legal aspects of deep learning

The paper dissects the intricacies of automated decision making (ADM) and urges for refining the current legal definition of artificial intelligence (AI) when pinpointing the role of algorithms in the advent of ubiquitous computing, data analytics and deep learning. Whilst coming up with a toolkit to measure algorithmic determination in automated/semi-automated tasks might be proven to be a tedious task for the legislator, our main aim here is to explain how a thorough understanding of the layers of ADM could be a first good step towards this direction: AI operates on a formula based on several degrees of automation employed in the interaction between the programmer, the user, and the algorithm. The paper offers a fresh look at AI, which exposes certain vulnerabilities in its current legal interpretation. To highlight this argument, analysis proceeds in two parts: Part 1 strives to provide a taxonomy of the various levels of automation that reflects distinct degrees of human–machine interaction. Part 2 further discusses the intricate nature of AI algorithms and considers how one can utilize observed patterns in acquired data. Finally, the paper explores the legal challenges that result from user empowerment and the requirement for data transparency

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

Robotics and the Lessons of Cyberlaw

Two decades of analysis have produced a rich set of insights as to how the law should apply to the Internet’s peculiar characteristics. But, in the meantime, technology has not stood still. The same public and private institutions that developed the Internet, from the armed forces to search engines, have initiated a significant shift toward developing robotics and artificial intelligence. This Article is the first to examine what the introduction of a new, equally transformative technology means for cyberlaw and policy. Robotics has a different set of essential qualities than the Internet and accordingly will raise distinct legal issues. Robotics combines, for the first time, the promiscuity of data with the capacity to do physical harm; robotic systems accomplish tasks in ways that cannot be anticipated in advance; and robots increasingly blur the line between person and instrument. Robotics will prove “exceptional” in the sense of occasioning systematic changes to law, institutions, and the legal academy. But we will not be writing on a clean slate: many of the core insights and methods of cyberlaw will prove crucial in integrating robotics and perhaps whatever technology follows

Sentencing decisions : the public view of the effects of consequences of crime, offender remorse and type of crime

The Australian justice system is based in a conventional model of justice with the aim of uniformity in sentencing. It is important to ascertain public opinion on the relevance of different factors to be taken into account at sentencing as accurately as possible, in order to provide informed public opinion which may assist policy makers in making legislation or educating the public on these matters. The current study examined the impact of varying levels of victim harm (high or low) and offender remorse (high or low) for both person and property crimes on sentencing decisions made by both male (n = 99) and female (n = 94) members of the Western Australian public. The design was a 2 x 2 x 2 x 2 between subjects factorial, with dependent variables of length of sentence assigned (0-10 years jail), rated influence of four sentencing goals (retribution, rehabilitation, incapacitation and deterrence) on sentence choice, and responses to an open-ended question about the reasons for the sentence chosen. The main findings were that demonstrations of offender remorse and the level of harm caused to the victim appeared to be factors in public participants\u27 sentencing. There was no difference in sentences assigned by male and female participants. Although the majority of participants believed they sentenced for rehabilitative reasons. Retribution appeared to be the major factor in the sentences assigned an outcome which reflects the focus of the Western Australian sentencing legislation. Implications arising from the results include the need for more public education in the areas of the functions or the courts, legal principles and theories, and options for victims of crime. Overall, the current study added to the body of research examining public opinions about the potential relevance of various victim and offender factors at the sentencing phase in the search for uniformity in sentencing