Giovanni Sartor

We shall introduce a set of fundamental legal concepts, providing a definition of each of them. This set will include, besides the usual deontic modalities (obligation, prohibition and permission), the following notions: obligative rights (rights related to other’s obligations), permissive rights, erga-omnes rights, normative conditionals, liability rights, different kinds of legal powers, potestative rights (rights to produce legal results), result-declarations (acts intended to produce legal determinations), and sources of the law.

I shall argue that software agents can be attributed cognitive states, since their behaviour can be best understood by adopting the intentional stance. These cognitive states are legally relevant when agents are delegated by their users to engage, without users’ review, in choices based on their the agents’ own knowledge. Consequently, both with regard to torts and to contracts, legal rules designed for humans can also be applied to software agents, even though the latter do not have rights and duties of their own. The implications of this approach in different areas of the law are then discussed, in particular with regard to contracts, torts, and personality.

This paper proposes an argumentation-based procedure for legal interpretation, by reinterpreting the traditional canons of textual interpretation in terms of argumentation schemes, which are then classified, formalized, and represented through argument visualization and evaluation tools. The problem of statutory interpretation is framed as one of weighing contested interpretations as pro and con arguments. The paper builds an interpretation procedure by formulating a set of argumentation schemes that can be used to comparatively evaluate the types of arguments used in cases of contested statutory interpretation in law. A simplified version of the Carneades Argumentation System is applied in a case analysis showing how the procedure works. A logical model for statutory interpretation is finally presented, covering pro-tanto and all-things-considered interpretive conclusions.

ABSTRACT Inspired by legal reasoning, this paper presents a semantics and proof theory of a system for defeasible argumentation. Arguments are expressed in a logic-programming language with both weak and strong negation, conflicts between arguments are decided with the help of priorities on the rules. An important feature of the system is that these priorities are not fixed, but are themselves defeasibly derived as conclusions within the system. Thus debates on the choice between conflicting arguments can also be modelled. The semantics of the system is given with a fixpoint definition, while its proof theory is stated in dialectical style, where a proof takes the form of a dialogue between a proponent and an opponent of an argument: an argument is shown to be justified if the proponent can make the opponent run out of moves in whatever way the opponent attacks.

This chapter proposes a dialectical approach to legal interpretation, consisting of three dimensions: (1) a formalization of the canons of interpretation in terms of argumentation schemes; (2) a dialectical classification of interpretive schemes; and (3) a logical and computational model for comparing the arguments pro and contra an interpretation. The traditional interpretive maxims or canons used in both common and civil law are translated into defeasible patterns of arguments, which can be evaluated through sets of corresponding critical questions. These interpretive argumentation schemes are classified in general categories and a distinction is drawn between schemes supporting and rebutting an interpretation. This framework allows conceiving statutory interpretation as a dialectical procedure consisting in weighing arguments pro and contra an interpretation. This procedure is formalized and represented computationally through tools from formal argumentation systems.

This chapter provides an analysis of defeasible legal reasoning as argumentation. It first provides a general account of the idea of defeasibility and introduces the idea of nonmonotonic reasoning. It then focuses on defeasible argumentation, considering how defeasible arguments can be constructed and how they can be defeated by rebutting and undercutting counterarguments. The dialectical interactions of defeasible arguments are further explored by focusing on reinstatement and reasoning about priorities. The idea of legal systems as the basis for argumentation frameworks is then investigated. The rationale for defeasibility in law is discussed, along with the possibility of using different approaches, such as revision or probability, to deal with uncertainty in legal reasoning. Finally, an account is provided of the emergence of theories of defeasibility in philosophy, logic, and legal theory.

This volume presents two Leibnizian writings, the Specimen of Philosophical Questions Collected from the Law and the Dissertation on Perplexing Cases. These works, originally published in 1664 and 1666, constitute, respectively, Leibniz's thesis for the title of Master of Philosophy and his doctoral dissertation in law. Besides providing evidence of the earliest development of Leibniz's thought and amazing anticipations of his mature views, they present a genuine intellectual interest, for the freshness and originality of Leibniz's reflections on a striking variety of logico-philosophical puzzles drawn from the law. The Specimen addresses puzzling issues resulting from apparent conflicts between law and philosophy (the latter broadly understood as comprising also mathematics, as well as empirical sciences). The Dissertation addresses cases whose solution is puzzling because of the convoluted logical form of legal dispositions and contractual clauses, or because of conflicting priorities between concurring parties. In each case, Leibniz dissects the problems with the greatest ingenuity, disentangling their different aspects, and proposing solutions always reasonable and sometimes surprising. And he does not refrain from peppering his intellectual acrobatics with some humorous comments. bbbbbb.

In this paper, we provide an analysis of the concept of legal personality and discuss whether personality may be conferred on artificial intelligence systems (AIs). Legal personality will be presented as a doctrinal category that holds together bundles of rights and obligations; as a result, we first frame it as a node of inferential links between factual preconditions and legal effects. However, this inferentialist reading does not account for the ‘background reasons’ of legal personality, i.e., it does not explain why we cluster different situations under this doctrinal category and how extra-legal information is integrated into it. We argue that one way to account for this background is to adopt a neoinstitutional perspective and to update the ontology of legal concepts with a further layer, the meta-institutional one. We finally argue that meta-institutional concepts can also support us in finding an equilibrium around the legal-policy choices that are involved in including (or not including) AIs among legal persons.

This handbook offers a deep analysis of the main forms of legal reasoning and argumentation from both a logical-philosophical and legal perspective. These forms are covered in an exhaustive and critical fashion, and the handbook accordingly divides in three parts: the first one introduces and discusses the basic concepts of practical reasoning. The second one discusses the main general forms of reasoning and argumentation relevant for legal discourse. The third one looks at their application in law as well as at the different areas of legal reasoning. The handbook’s division in three parts reflects its conceptual architecture, since legal reasoning and argumentation are considered in relation to the more general types of reasoning.

Beginning with a concise reflection on the significance of digital sovereignty, this chapter describes and problematizes the Chinese social credit system in connection with the notion of digital sovereignty. It provides a synthetic illustration of the origin, evolution, essential characteristics, structure, explicit aims, and main limitations of the Chinese social credit system, taking into account the specific historical and ideological context in which the system has been designed and developed. On the basis of this description, the potentialities, still to be realised, of the use of artificial intelligence techniques in the different applications related to surveillance, evaluation, and sanctioning are assessed and discussed, in relation to the Chinese discourse on digital sovereignty. The last part of the chapter discusses the potential consequences of such a system in terms of individual rights, individual self-determination, and autonomy, which in turn influence the construction of the entire social fabric.

This chapter introduces a conceptual framework for qualitative risk assessment of AI, particularly in the context of the EU AI Act. The framework addresses the complexities of legal compliance and fundamental rights protection by integrating definitional balancing and defeasible reasoning. Definitional balancing employs proportionality analysis to resolve conflicts between competing rights, while defeasible reasoning accommodates the dynamic nature of legal decision-making. Our approach stresses the need for an analysis of AI deployment scenarios and for identifying potential legal violations and multi-layered impacts on fundamental rights. On the basis of this analysis, we provide philosophical foundations for a logical account of AI risk analysis. In particular, we consider the basic building blocks for conceptually grasping the interaction between AI deployment scenarios and fundamental rights, incorporating in defeasible reasoning definitional balancing and arguments about the contextual promotion or demotion of rights. This layered approach allows for more operative models of assessment of both high-risk AI systems and General Purpose AI (GPAI) systems, emphasizing the broader applicability of the latter. Future work aims to develop a formal model and effective algorithms to enhance AI risk assessment, bridging theoretical insights with practical applications to support responsible AI governance.

This article advances an explanatory model of the academic and policy debate on AI as legal persons. It argues that the scientific and regulatory debate on AI as legal persons undergoes periods of relative stability interrupted by rapid paradigm shifts. Three interrelated factors primarily influence these oscillations: (1) competing theories of legal personhood (clustered versus singularist), (2) capability, embodiment, and commercial reach of AI technology, and (3) AI's integration within socio-digital institutions. Two additional forces modulate the depth and durability of any shift: (4) the overlap and internal consistency of cross‑domain legal regimes (data protection, agency, liability, cybersecurity); and (5) the body of judicial precedents (or their scarcity) on extending personhood. Specific convergences of these factors create pressure for status reconsideration; absent them, equilibrium returns. Current EU law and case practice are explained by partial convergence—capability/diffusion surges and some cluster‑theory uptake without sufficiently embedding AI in rights‑bearing institutional roles—and by the damping effect of dense, interlocking legal regimes, plus scarce and negative judicial precedents. Case law suggests that, without explicit legislation, courts are unlikely to grant AI legal personhood on their own. Prospective movement would require demonstrable de facto social personhood: sustained, role‑based participation within socio-digital institutions that creates governance gaps that existing doctrines cannot internalize. The article concludes by assessing near- and long‑term prospects — from generative AI, autonomous agents, social robots, and digital twins to brain–machine interface — highlighting conditional pathways rather than deterministic trajectories.

In this paper we provide a formal analysis of the idea of normative co-ordination. We argue that this idea is based on the assumption that agents can achieve flexible co-ordination by conferring normative positions to other agents. These positions include duties, permissions, and powers. In particular, we explain the idea of declarative power, which consists in the capacity of the power-holder of creating normative positions, involving other agents, simply by proclaiming such positions. In addition, we account also for the concepts of representation, namely the representatives capacity of acting in the name of his principal, and of mandate, which is the mandatees duty to act as the mandator has requested. Finally, we show how the framework can be applied to represent the contract-net protocol. Some brief remarks on future research and applications conclude this contribution.

Machine learning classifiers are increasingly used to inform, or even make, decisions significantly affecting human lives. Fairness concerns have spawned a number of contributions aimed at both identifying and addressing unfairness in algorithmic decision-making. This paper critically discusses the adoption of group-parity criteria (e.g., demographic parity, equality of opportunity, treatment equality) as fairness standards. To this end, we evaluate the use of machine learning methods relative to different steps of the decision-making process: assigning a predictive score, linking a classification to the score, and adopting decisions based on the classification. Throughout our inquiry we use the COMPAS system, complemented by a radical simplification of it (our SAPMOC I and SAPMOC II models), as our running examples. Through these examples, we show how a system that is equally accurate for different groups may fail to comply with group-parity standards, owing to different base rates in the population. We discuss the general properties of the statistics determining the satisfaction of group-parity criteria and levels of accuracy. Using the distinction between scoring, classifying, and deciding, we argue that equalisation of classifications/decisions between groups can be achieved thorough group-dependent thresholding. We discuss contexts in which this approach may be meaningful and useful in pursuing policy objectives. We claim that the implementation of group-parity standards should be left to competent human decision-makers, under appropriate scrutiny, since it involves discretionary value-based political choices. Accordingly, predictive systems should be designed in such a way that relevant policy goals can be transparently implemented. Our paper presents three main contributions: (1) it addresses a complex predictive system through the lens of simplified toy models; (2) it argues for selective policy interventions on the different steps of automated decision-making; (3) it points to the limited significance of statistical notions of fairness to achieve social goals.

Reasoning with cases has been a primary focus of those working in AI and law who have attempted to model legal reasoning. In this paper we put forward a formal model of reasoning with cases which captures many of the insights from that previous work. We begin by stating our view of reasoning with cases as a process of constructing, evaluating and applying a theory. Central to our model is a view of the relationship between cases, rules based on cases, and the social values which justify those rules. Having given our view of these relationships, we present our formal model of them, and explain how theories can be constructed, compared and evaluated. We then show how previous work can be described in terms of our model, and discuss extensions to the basic model to accommodate particular features of previous work. We conclude by identifying some directions for future work.

In a world of human-only readers, a trade-off persists between comprehensiveness and comprehensibility: only privacy policies too long to be humanly readable can precisely describe the intended data processing. We argue that this trade-off no longer exists where LLMs are able to extract tailored information from clearly-drafted fully-comprehensive privacy policies. To substantiate this claim, we provide a methodology for drafting comprehensive non-ambiguous privacy policies and for querying them using LLMs prompts. Our methodology is tested with an experiment aimed at determining to what extent GPT-4 and Llama2 are able to answer questions regarding the content of privacy policies designed in the format we propose. We further support this claim by analyzing real privacy policies in the chosen market sectors through two experiments (one with legal experts, and another by using LLMs). Based on the success of our experiments, we submit that data protection law should change: it must require controllers to provide clearly drafted, fully comprehensive privacy policies from which data subjects and other actors can extract the needed information, with the help of LLMs.