1,721,245 research outputs found
Value assessment and revision in legal interpretation
No full textThe research aims at a formal definition of constructive interpretation in law as the dynamic of revision of theories about the normative system, embedding a model of balancing values [13] into an architecture of i/o logics representing conceptual, deontological and axiological rules [11]. We also introduce new revision operators which are relevant in the context of value assessments
A novel approach for a ceteris paribus deontic logic
Full text linkWe present a formal semantics for deontic logic based on the concept of ceteris paribus preferences. It allows to introduce notions of conditional/unconditional obligation and permission that are interpreted relative to this semantics. We show how obligations and permissions can be represented compactly using existing preference frameworks from the artificial intelligence area
A probabilistic deontic argumentation framework
No full textWhat does it mean that something is probably obligatory? And how does it relate to the probability that it is permitted or prohibited? In this paper, we provide a possible answer by merging deontic argumentation and probabilistic argumentation into a probabilistic deontic argumentation framework. This framework allows us to specify a semantics for the probability of deontic statuses. The deontic argumentation part builds on standard concepts from the study of computational models of argument: rule-based arguments, argumentation graphs, argument labelling semantics and statement labelling semantics. We then encapsulate this deontic composition with the approach of probabilistic labellings to probabilistic argumentation, in order to associate deontic statements with probability values. The framework is illustrated with a scenario featuring a violation and a contrary-to-duty obligation
The Regulation of Content Moderation
No full textOnline platforms have become a key infrastructure for creating and sharing content, thus representing a paramount context for the individual/collective exercise of fundamental rights (e.g., freedom of expression, association) and the realisation of social values (citizens’ information, education, democratic dialogue). At the same time, platforms offer new opportunities for unfair or harmful behaviours, such as the unauthorised distribution of copyrighted content, privacy violation, unlawful content distribution (e.g., hate speech, child pornography), and fake news. To prevent or at least mitigate the spread of such content, online platforms have been encouraged to resort to content moderation. This activity uses automated systems to govern content flows to ensure lawful and productive user interactions. These systems deploy state-of-the-art AI technologies (e.g., deep learning, NLP) to detect prohibited content and restrict its further dissemination. In this Chapter, we will address the use of automated systems in content moderation and the related regulatory aspects. Section 2 will provide a general overview of content moderation on online platforms, focusing mainly on automated filtering. Further, Sect. 3 will describe existing techniques for automatically filtering content. Section 4 will discuss some critical challenges in automated content moderation, namely vulnerability, failures in accuracy, subjectivity and discrimination. Furthermore, Sect. 5 will define some of the steps needed to regulate moderation. Finally, in Sect. 6, we will review existing legislation that addresses content moderation in online environments
Evaluation of causal arguments in law: The case of overdetermination
No full textIn many legal disputes, determining and evaluating cause-in-fact is a crucial step in the liability attribution. It is, however, difficult and opaque. In this paper, we analyse the cases of overdetermination, where there is more than one cause for the outcome. The proposed framework (FCA) employs logic-based argument modelling. It distinguishes individual contributors in overdetermination cases by using a new set of critical questions based on argument schemes from effect-to-cause. To illustrate the use of the FCA, the Heneghan v Manchester Dry Docks lung cancer case with multi-party contributions is analysed
'Hard AI crime' : the deterrence turn
Full text linkPublished online: 07 May 2024Machines powered by artificial intelligence (AI) are increasingly taking over tasks previously performed by humans alone. In accomplishing such tasks, they may intentionally commit ‘AI crimes’, ie engage in behaviour which would be considered a crime if it were accomplished by humans. For instance, an advanced AI trading agent may—despite its designer’s best efforts—autonomously manipulate markets while lacking the properties for being held criminally responsible. In such cases (hard AI crimes) a criminal responsibility gap emerges since no agent (human or artificial) can be legitimately punished for this outcome. We aim to shift the ‘hard AI crime’ discussion from blame to deterrence and design an ‘AI deterrence paradigm’, separate from criminal law and inspired by the economic theory of crime. The homo economicus has come to life as a machina economica, which, even if cannot be meaningfully blamed, can nevertheless be effectively deterred since it internalises criminal sanctions as costs.The work has been supported by the “CompuLaw” project, funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No.833647
The Burden of Persuasion in Abstract Argumentation
Full text linkIn this paper, we provide a formal framework for modeling the burden of persuasion in legal reasoning. The framework is based on abstract argumentation, a frequently studied method of non-monotonic reasoning, and can be applied to different argumentation semantics; it supports burdens of persuasion with arbitrary many levels, and allows for the placement of a burden of persuasion on any subset of an argumentation framework’s arguments. Our framework can be considered an extension of related works that raise questions on how burdens of persuasion should be handled in some conflict scenarios that can be modeled with abstract argumentation. An open source software implementation of the introduced formal notions is available as an extension of an argumentation reasoning library
How the Law Has Become Computable
No full textIn this chapter we will examine the different ways in which law has progressively merged with computing, through research and applications in legal informatics. The goal is twofold: on the one hand to show that legal informatics builds upon legal premises (content, processes, concepts and theories), and on the other hand to show how research in legal informatics can make a contribution to legal studies (at the doctrinal, and theoretical level).
We will distinguish different aspects of law which have been modelled for the purpose of computation, and the different ways in which the researcher or developer of legal informatics applications have adressed the law.
In particular we shall show how research meant to provide computable models of the law – document retrieval, conceptual constructions (ontologies), logical reasoning, argumentation, symbolic (rule-based) or sub-symbolic (data-driven) inference – has addressed of different aspects of legal knowledge and cognition
A genetic approach to the ethical knob
Full text linkAs Autonomous vehicles (AVs) are entering shared roads, the challenge of designing and implementing a completely autonomous vehicle is still open. Aside from technological issues regarding how to manage the complexity of the environment, AVs raise difficult legal issues and ethical dilemmas, especially in unavoidable accident scenarios. In this context, a vast speculation depicting moral dilemmas has developed in recent years. A new perspective was proposed: an 'Ethical Knob' (EK), enabling passengers to ethically customise their AVs, namely, to choose between different settings corresponding to different moral approaches or principles. In this contribution we explore how an AV can automatically learn to determine the value of its 'Ethical Knob' in order to achieve a trade-off between the ethical preferences of passengers and social values, learning from experienced instances of collision. To this end, we propose a novel approach based on a genetic algorithm to optimize a population of neural networks. We report a detailed description of simulation experiments as well as possible applications
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