Privacy policies often fall short of providing a comprehensive account of how personal data is used, thus failing to comply with GDPR requirements. By doing so, they hamper the users’ ability to make informed decisions about using services while ensuring that their data is used properly and fairly. This calls for automatic tools that can effectively identify potentially unlawful policies. Here we present a new corpus of Italian privacy policies, with clauses labelled by experts in data protection law, to indicate the level of comprehensiveness of information. We focus on the categories of data processed, classifying each clause as either sufficiently or insufficiently informative (“vague”). We perform 6 different classification and detection tasks, comparing the performance of BERT-based models and generative Large Language Models. Addressing multilingualism is crucial in the EU, whose 24 spoken languages are an integral part of its cultural heritage. Consequentely, we also perform cross-language experiments to evaluate whether a pre-existing English corpus or classifiers can be leveraged for Italian and, vice versa, whether our corpus is informative enough to generalize to other languages.

Causation plays a central role in the attribution of responsibility, especially in the legal domain, where complex causal scenarios frequently arise. Traditionally, legal reasoners have relied on the idea that a cause must be a necessary condition of its effect, which falls short in scenarios involving overdetermination, preemption, or omission, thereby failing to adequately identify causes-in-fact. In this paper, we present a novel analysis of selected legal cases, each exemplifying common causal dilemmas discussed in causal literature. We employ three different notions of cause in our analysis: abductive explanation (AXp), the NESS test (Necessary Element of a Sufficient Set) and actual cause. We express the three notions and some of their variants in a modal language for causal reasoning that we interpret on a rule-based semantics. We provide a model checking algorithm for our modal language relying on a reduction into TQBF as well as an implementation of the legal cases in our causal model checker to automatically verify “what is the cause of what” and what types of causes apply in each legal case. Our interdisciplinary approach highlights the usefulness of logic-based methods for legal analysis, offering a fully transparent model-checking toolbox that could potentially support legal reasoners in disentangling complex factual scenarios.

Unfair clause detection is an extremely useful AI application for consumer protection. Artificial intelligence has recently been successful in building systems capable to automatically detect unfair clauses in Terms of Service, and also to identify their unfairness categories. Since Large Language Models (LLMs) are nowadays bringing a revolution to the field of artificial intelligence, and in particular to natural language processing and understanding, in this paper we compare several different prompt strategies for LLMs with more traditional BERT-based fine-tuned models. Our extensive experimental evaluation aims to investigate whether it is worth using LLMs also for this challenging domain-specific task.

We propose to model cause-in-fact in legal cases through fresh argumentation-theoretic notions of explanation and support, meant to capture the set of arguments that contribute to making a conclusion justified. This novel argumentation-based approach to causality in law goes beyond the traditional idea of a cause as a necessary antecedent condition (the conditio-sine-qua-non idea), to handle concurrent causal processes leading to overdetermination and preemption. It also provides sound analyses of cases involving omission and ennoblement. Finally, by relying on defeasible argumentation it can capture causal inferences based on defeasible generalisations, which are very often used in judicial reasoning. Through the analysis of causal puzzles in legal cases, we illustrate the framework’s effectiveness in handling complex causal reasoning, and demonstrate its potential to support legal reasoners with structured and intuitive analysis.

Despite some improvements in compliance metrics after the implementation of the European General Data Protection Regulation (GDPR), privacy policies have become longer and more ambiguous. They often fail to fully meet GDPR requirements, thus leaving users without a reliable way to understand how their data is processed. We present a novel corpus composed by 30 privacy policies of online platforms and a new set of annotation guidelines, to assess the level of comprehensiveness of information. We focus on the processed categories of data, classifying each clause either as fully informative or as insufficiently informative. In our experimental evaluation, we perform 6 different classification and detection tasks, comparing BERT models and generative Large Language Models.

Causality is vital for establishing legal liability, but traditional analyses often fail to address complex scenarios and conflate causation with legal responsibility. This paper presents a novel approach to cause-in-fact based on argumentation theory which also assesses whether a cause-in-fact lacks legal relevance.

Causal reasoning is a challenging topic not only in philosophy, science and in theories of human mind, but also in legal reasoning. Causality is indeed a key precondition for civil and criminal liability, in all cases dealing with the connection between human actions or omissions and harmful events. Only a partial overlap exists between natural causality (cause-in-fact) and legal causality: there are instances in which what appears to be a natural cause is not recognised as a legal one, as well as instances in which causality may be presumptively ascribed by the law in the absence of decisive evidence for natural causality. Legal policy considerations may explain these puzzling divergences, as we will discuss in the following. In this paper, we use argumentation schemes to provide simple and intuitive patterns for assessing causality in the legal domain. The analysis of these argument schemes will enable us to clarify some connections between natural and legal causation. Our schemes will include the necessary condition (but-for), overdetermination (NESS), preemption, presumptions based on the increase of risks or presumption based on statutory obligations, and interruption of causality due to unexpected events (Actus Novus). These approaches are tested on the basis of real legal cases in different domains.

In 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.