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.

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.

Deploying Large Language Models (LLMs) for legal question-answering requires ensuring factual accuracy and logical coherence. Current evaluation metrics inadequately capture legal reasoning complexity, while expert assessments lack scalability. We propose a two-dimensional framework that independently measures Truthfulness and Reasoning Soundness in model outputs, applied to Italian asylum proceedings requiring evidence-based analysis. This dual-axis approach reveals critical issues—such as legally correct answers derived through unsound or hallucinatory reasoning—that standard metrics fail to detect. To enable large-scale application, we implement an automated LLM-as-a-Judge system with bias-mitigation techniques. Experimental results demonstrate strong correspondence between automated judgments and expert evaluations, confirming framework reliability. This work advances diagnostic methodology for assessing LLMs in legal domains, offering both theoretical insight and practical applicability toward more trustworthy and accountable legal AI systems.

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.

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.

Argument structure prediction aims to identify the relations between arguments or between parts of arguments. It is a crucial task in legal argument mining, where it could help identifying motivations behind judgments or even fallacies or inconsistencies. It is also a very challenging task, which is relatively underdeveloped compared to other argument mining tasks, owing to a number of reasons including a low availability of datasets and a high complexity of the reasoning involved. In this work, we address argumentative link prediction in decisions by Court of Justice of the European Union on fiscal state aid. We study how propositions are combined in higher-level structures and how the relations between propositions can be predicted by NLP models. To this end, we present a novel annotation scheme and use it to extend a dataset from literature with an additional annotation layer. We use our new dataset to run an empirical study, where we compare two architectures and explore different combinations of hyperparameters and training regimes. Our results indicate that an ensemble of residual networks yields the best results.

This study aims at predicting the outcomes of legal cases based on the textual content of judicial decisions. We present a new corpus of Italian documents, consisting of 226 annotated decisions on Value Added Tax by Regional Tax law commissions. We address the task of predicting whether a request is upheld or rejected in the final decision. We employ traditional classifiers and NLP methods to assess which parts of the decision are more informative for the task.

We present a study aimed at testing the CLAUDETTE system’s ability to generalise the concept of unfairness in consumer contracts across diverse market sectors. The data set includes 142 terms of services grouped in five sub-sets: travel and accommodation, games and entertainment, finance and payments, health and well-being, and the more general others. Preliminary results show that the classifier has satisfying performance on all the sectors.

As 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.

Recent years have been tainted by market practices that continuously expose us, as consumers, to new risks and threats. We have become accustomed, and sometimes even resigned, to businesses monitoring our activities, examining our data, and even meddling with our choices. Artificial Intelligence (AI) is often depicted as a weapon in the hands of businesses and blamed for allowing this to happen. In this paper, we envision a paradigm shift, where AI technologies are brought to the side of consumers and their organizations, with the aim of building an efficient and effective counter-power. AI-powered tools can support a massive-scale automated analysis of textual and audiovisual data, as well as code, for the benefit of consumers and their organizations. This in turn can lead to a better oversight of business activities, help consumers exercise their rights, and enable the civil society to mitigate information overload. We discuss the societal, political, and technological challenges that stand before that vision.

Different formalisms for defeasible reasoning have been used to represent legal knowledge and to reason with it. In this work, we provide an overview of the following logic-based approaches to defeasible reasoning: Defeasible Logic, Answer Set Programming, ABA+, ASPIC+, and DeLP. We compare features of these approaches from three perspectives: the logical model (knowledge representation), the method (computational mechanisms), and the technology (available software). On this basis, we identify and apply criteria for assessing their suitability for legal applications. We discuss the different approaches through a legal running example.

Consumer contracts often contain unfair clauses, in apparent violation of the relevant legislation.In this paper we present a new methodology for evaluating such clauses in online Terms of Services.We expand a set of tagged documents (terms of service), with a structured corpus where unfair clauses are liked to a knowledge base of rationales for unfairness, and experiment with machine learning methods on this expanded training set.Our experimental study is based on deep neural networks that aim to combine learning and reasoning tasks, one major example being Memory Networks.Preliminary results show that this approach may not only provide reasons and explanations to the user, but also enhance the automated detection of unfair clauses.

Artificial Intelligence and Law is undergoing a critical transformation. Traditionally focused on the development of expert systems and on a scholarly effort to develop theories and methods for knowledge representation and reasoning in the legal domain, this discipline is now adapting to a sudden change of scenery. No longer confined to the walls of academia, it has welcomed new actors, such as businesses and companies, who are willing to play a major role and seize new opportunities offered by the same transformational impact that recent AI breakthroughs are having on many other areas. As it happens, commercial interests create new opportunities but they also represent a potential threat to consumers, as the balance of power seems increasingly determined by the availability of data. We believe that while this transformation is still in progress, time is ripe for the next frontier of this field of study, where a new shift of balance may be enabled by tools and services that can be of service not only to businesses but also to consumers and, more generally, the civil society. We call that frontier consumer-empowering AI.

Two years after its entry into force, the EU General Data Protection Regulation became applicable on the 25th May 2018. Despite the long time for preparation, privacy policies of online platforms and services still often fail to comply with information duties and the standard of lawfulness of data processing. In this paper we present a new methodology for processing privacy policies under GDPR's provisions, and a novel annotated corpus, to be used by machine learning systems to automatically check the compliance and adequacy of privacy policies. Preliminary results confirm the potential of the methodology.

Consumer contracts too often present clauses that are potentially unfair to the subscriber. We present an experimental study where machine learning is employed to automatically detect such potentially unfair clauses in online contracts. Results show that the proposed system could provide a valuable tool for lawyers and consumers alike.