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.

This paper addresses the extraction of Judicial Interpretative Formulas (JIFs) in decisions of the Court of Justice of the European Union (CJEU) on Value Added Tax (VAT). European case law includes a significant number of JIFs on this subject, which are crucial for the interpretation of VAT. However, extracting such JIFs manually is effortful, and doing that automatically has not been investigated yet in the VAT domain. Our work proposes the first pipeline method for doing so. We start by defining a set of guidelines for annotating legal texts following a principle definition of JIF. By following such guidelines, we obtain a corpus of 21 expert-labeled CJEU decisions. We keep them for validation and testing. For training, we machine-annotate 80 additional decisions using LLMs. Our experiments show that BERT-based architectures trained on such data perform comparably to LLMs.

Current research in machine learning and artificial intelligence is largely centered on modeling and performance evaluation, less so on data collection. However, recent research demonstrated that limitations and biases in data may negatively impact trustworthiness and reliability. These aspects are particularly impactful on sensitive domains such as mental health and neurological disorders, where speech data are used to develop AI applications for patients and healthcare providers. In this paper, we chart the landscape of available speech datasets for this domain, to highlight possible pitfalls and opportunities for improvement and promote fairness and diversity. We present a comprehensive list of desiderata for building speech datasets for mental health and neurological disorders and distill it into an actionable checklist focused on ethical concerns to foster more responsible research.

We present ACME: A Chatbot for asylum-seeking Migrants in Europe. ACME relies on computational argumentation and aims to help migrants identify the highest level of protection they can apply for. This would contribute to a more sustainable migration by reducing the load on territorial commissions, Courts, and humanitarian organizations supporting asylum applicants. We describe the background context, system architecture, underlying technologies, and a case study used to validate the tool with domain experts.

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.

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.

We explore the use of residual networks and neural attention for multiple argument mining tasks. We propose a residual architecture that exploits attention, multi-task learning, and makes use of ensemble, without any assumption on document or argument structure. We present an extensive experimental evaluation on five different corpora of user-generated comments, scientific publications, and persuasive essays. Our results show that our approach is a strong competitor against state-of-the-art architectures with a higher computational footprint or corpus-specific design, representing an interesting compromise between generality, performance accuracy and reduced model size.

AMICA is an argument mining-based search engine, specifically designed for the analysis of scientific literature related to Covid-19. AMICA retrieves scientific papers based on matching keywords and ranks the results based on the papers' argumentative content. An experimental evaluation conducted on a case study in collaboration with the Italian National Institute of Health shows that the AMICA ranking agrees with expert opinion, as well as, importantly, with the impartial quality criteria indicated by Cochrane Systematic Reviews.

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.

Attention is an increasingly popular mechanism used in a wide range of neural architectures. The mechanism itself has been realized in a variety of formats. However, because of the fast-paced advances in this domain, a systematic overview of attention is still missing. In this article, we define a unified model for attention architectures in natural language processing, with a focus on those designed to work with vector representations of the textual data. We propose a taxonomy of attention models according to four dimensions: the representation of the input, the compatibility function, the distribution function, and the multiplicity of the input and/or output. We present the examples of how prior information can be exploited in attention models and discuss ongoing research efforts and open challenges in the area, providing the first extensive categorization of the vast body of literature in this exciting domain.

We study annotation projection in text classification problems where source documents are published in multiple languages and may not be an exact translation of one another.In particular, we focus on the detection of unfair clauses in privacy policies and terms of service.We present the first English-German parallel asymmetric corpus for the task at hand.We study and compare several language-agnostic sentence-level projection methods.Our results indicate that a combination of word embeddings and dynamic time warping performs best.

Special issue of Teams in Multiagent Systems

[No abstract available]

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.

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.

This special issue of Fundamenta Informaticae publishes extended and revised versions of the best papers presented at the 1st edition of the track on Knowledge Representation and Reasoning (

This special issue of Fundamenta Informaticae publishes extended and revised versions of the best papers presented at the 24th RCRA International Workshop (RCRA 2017). 1 This event follows the series of the RCRA (the working group of the AI*IA association on Knowledge Representation and Automated Reasoning) annual meetings, held since 1994, and that since 2007 became an international workshop.RCRA 2017 was held in Bari, Italy, on 14th November 2017 as a satellite workshop of the 16th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2017).The success of all these events shows that RCRA is nowadays established as a major forum for exchanging ideas and proposing experimentation methodologies for algorithms in Artificial Intelligence.vi iii possibility and for his support throughout the whole process.Finally, we are very grateful to the Program Committee members and to the external anonymous reviewers of RCRA 2017 for their work, as well as to the authors and the participants that took part in the workshop.

Argument-based decision making has been employed to support a variety of reasoning tasks over medical knowledge. These include evidence-based justifications of the effects of treatments, the detection of conflicts in the knowledge base, and the enabling of uncertain and defeasible reasoning in the health-care sector. However, a common limitation of these approaches is that they rely on structured input information. Recent advances in argument mining have shown increasingly accurate results in detecting argument components and predicting their relations from unstructured, natural language texts. In this study, we discuss evidence and claim detection from Randomized Clinical Trials. To this end, we create a new annotated dataset about four different diseases (glaucoma, diabetes, hepatitis B, and hypertension), containing 976 argument components (697 containing evidence, 279 claims). Empirical results are promising, and show the portability of the proposed approach over different branches of medicine.

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.

Agent-based computing addresses the challenges in managing distributed computing systems and networks through monitoring, communication, consensus-based decision-making and coordinated actuation.As a result, intelligent agents and multi-agent systems have demonstrated the capability to use intelligence, knowledge representation and reasoning, and other social metaphors like 'trust', 'game' and 'institution', not only to address real-world problems in a human-like way but also to transcend human performance.This has had a transformative impact in many application domains, particularly in e-commerce, and also in planning, logistics, manufacturing, robotics, decision support, transportation, entertainment, emergency relief & disaster management, and data mining & analytics.As one of the largest and still growing research fields of Computer Science, agent-based computing today remains a unique enabler of inter-, multi-and trans-disciplinary research.The International Conference on Conference on Principles and Practice of Multi-Agent Systems (PRIMA) originally started in 1998 as a regional (Asia-Pacific) workshop and in the last decade it grew to become one of the leading and influential scientific conferences for research on multi-agent systems.Each year, PRIMA brings together active researchers, developers and practitioners from both academia and industry to showcase, share and promote research in several domains, ranging from foundations of agent theory and engineering aspects of agent systems, to emerging interdisciplinary areas of agent-based research.Previous successful editions were held in Nagoya, Japan (2009), Kolkata, India (2010), Wollongong, Australia (2011), Kuching, Malaysia (2012), Dunedin, New Zealand (2014), and Gold Coast Australia (2014).The last two editions were held in Phuket, Thailand (2016) and in Nice, France (2017).This issue contains selected papers from the eighteenth edition of PRIMA, which took place from 26 to 30 October 2015 in Bertinoro, FC, Italy.The conference received 94 submissions from 30 countries.From the 29 papers presented at the conference, the best theoretically-oriented ones were invited to submit an extended version for this special issue with Fundamenta Informaticae, while another special issue with the Journal of Agent-Oriented Software Engineering has been organized around the more practically-oriented papers.The contributions that were eventually submitted underwent a thorough two-or three-stage reviewing procedure, resulting in the ten papers in the present collection.They constitute the most recent advances in the theory and practice of multi-agent systems, with interesting intersections with other domains.

The fourteenth International Workshop on Computational Logic in Multi-Agent Systems (CLIMA XIV) was held in Coruña, Spain, 16–18 September 2013. The final programme included 23 papers and 30 participants attended the workshop. This special issue contains six papers from the workshop that discuss a variety of issues central to the use of logic in reasoning about multi-agent systems. The first paper in the collection, ‘The Equivalence Zoo for Dung-style Semantics’ by Baumann and Brewka, presents an extensive study of seven equivalence notions (standard, normal, strong, weak, and local expansion and minimal change equivalence) under major semantics of Dung's argumentation framework (stable, preferred, admissible and complete semantics). It shows that minimal change equivalence is a reasonable notion of equivalence between argumentation frameworks. The paper also investigates the aforementioned relationship with respect to the two restricted classes of argumentation frameworks that have the same arguments and/or are self-loop-free. The second paper, ‘Two-stage Agent Program Verification’ by Dennis, Fisher and Webster, proposes a novel method for verification of agent programs that are written in a Belief–Desire–Intention (BDI) programming language using program model-checkers. The paper extends the Agent Java Pathfinder (AJPF) agent program model-checker to generate models that could be used by other model-checkers. The key idea behind the approach lies in that generated models could be used for several purposes (e.g. proving different properties of a program). The paper demonstrates the new technique by describing the export of the AJPF program models to both the SPIN and P rism model-checkers.

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.

No abstract available.

The automatic extraction of arguments from text, also known as argument mining, has recently become a hot topic in artificial intelligence. Current research has only focused on linguistic analysis. However, in many domains where communication may be also vocal or visual, paralinguistic features too may contribute to the transmission of the message that arguments intend to convey. For example, in political debates a crucial role is played by speech. The research question we address in this work is whether in such domains one can improve claim detection for argument mining, by employing features from text and speech in combination. To explore this hypothesis, we develop a machine learning classifier and train it on an original dataset based on the 2015 UK political elections debate.

This special issue of Fundamenta Informaticae publishes extended and revised versions of the best papers orally presented at the 22nd RCRA International Workshop (RCRA 2015). 1 This event follows the series of the RCRA (the working group of the AI*IA association on Knowledge Representation and Automated Reasoning) annual meetings, held since 1994, and that from 2007 became an international workshop.RCRA 2015 was held in Ferrara, Italy, on 22 September 2015 as a satellite workshop of the 14th Conference of the Italian Association for Artificial Intelligence (AI*IA 2015).The success of all these events shows that RCRA is nowadays established as a major forum for exchanging ideas and proposing experimentation methodologies for algorithms in Artificial Intelligence.

Argumentation mining aims at automatically extracting structured arguments from unstructured textual documents. It has recently become a hot topic also due to its potential in processing information originating from the Web, and in particular from social media, in innovative ways. Recent advances in machine learning methods promise to enable breakthrough applications to social and economic sciences, policy making, and information technology: something that only a few years ago was unthinkable. In this survey article, we introduce argumentation models and methods, review existing systems and applications, and discuss challenges and perspectives of this exciting new research area.

Micro debates App for Android is a part of a research effort aimed to propose better ways of exchanging ideas and opinions in online communities. With it, a user can argue from a handheld device, using Twitter. One can also visualize opinions of other micro debaters, explore ongoing debates, and see where the consensus is. Under the hood, Micro debates uses computational argumentation to rank opinions and drive the visualization. The result is a visual summary of the debate that takes into account semantic information such as explicit attack relations that link opinions together. To the best of our knowledge, this is the first application that brings computational argumentation to handheld devices. We describe the application and its logic, and discuss results from an empirical study.

João Leite, Paolo Torroni, Thomas Ågotnes, Guido Boella, Leendert van der Torre; Preface to the Special Issue on Computational Logic in Multi-Agent Systems (CLI

We propose $${\mathcal{B}}$$ -Tropos as a modeling framework to support agent-oriented systems engineering, from high-level requirements elicitation down to execution-level tasks. In particular, we show how $${\mathcal{B}}$$ -Tropos extends the Tropos methodology by means of declarative business constraints, inspired by the ConDec graphical language. We demonstrate the functioning of $${\mathcal{B}}$$ -Tropos using a running example inspired by a real-world industrial scenario, and we describe how $${\mathcal{B}}$$ -Tropos models can be automatically formalized in computational logic, discussing formal properties of the resulting framework and its verification capabilities.

Despite their dynamic nature, social commitments have rarely been used for monitoring purposes. Little attention has been paid to the relationship between commitments and the temporal dimension and to the corresponding run-time verification. Building on previous work, we present a declarative axiomatization of time-aware social commitments, extending their basic life cycle with time-related transitions and compensation mechanisms. The formalization is based on a reactive version of the event calculus, able to monitor the commitment's evolution during a system's execution, to check whether the interacting agents are honoring them or not. The resulting monitoring framework can be used in the context of agent-based simulation, either to dynamically evaluate whether a running simulation is compliant with a commitment-based contract or to provide useful information to the interacting agents, helping them to behave in a compliant manner.

Since its introduction, the Event Calculus (ℰ𝒞) has been recognized for being an excellent framework to reason about time and events, and it has been applied to a variety of domains. However, its formalization inside logic-based frameworks has been

We discuss the static verification of declarative Business Processes. We identify four desiderata about verifiers, and propose a concrete framework which satisfies them. The framework is based on the ConDec graphical notation for modeling Business Processes, and on Abductive Logic Programming technology for verification of properties. Empirical evidence shows that our verification method seems to perform and scale better, in most cases, than other state of the art techniques (model checkers, in particular). A detailed study of our framework’s theoretical properties proves that our approach is sound and complete when applied to ConDec models that do not contain loops, and it is guaranteed to terminate when applied to models that contain loops.

In semantic Web technologies, searching for a service means to identify components that can potentially satisfy the user needs in terms of outputs and effects (discovery), and that, when invoked by the customer, can fruitfully interact with her (contracting). In this paper, we present an application framework that encompasses both the discovery and the contracting steps, in a unified search process. In particular, we accommodate service discovery by ontology-based reasoning, and contracting by automated reasoning about policies published in a formal language. To this purpose, we consider a formal approach grounded on computational logic, and abductive logic programming in particular. We propose a framework, called SCIFF reasoning engine, able to establish, by ontological and abductive reasoning, if a semantic Web service and a requester can fruitfully inter-operate, taking as input the behavioral interfaces of both the participants, and producing as output a sort of a contract.

SCIFF is a framework thought to specify and verify interaction in open agent societies. The SCIFF language is equipped with a semantics based on abductive logic programming; SCIFF's operational component is a new abductive logic programming proof procedure, also named SCIFF, for reasoning with expectations in dynamic environments. In this article we present the declarative and operational semantics of the SCIFF language, and the termination, soundness, and completeness results of the SCIFF proof procedure, and we demonstrate SCIFF's possible application in the multiagent domain.

The concept of anagentis increasingly used in contemporary software applications, particularly those involving the Internet, autonomous systems, or cooperation. However, with dependability and safety in mind, it is vital that the mechanisms for representing and implementing agents are clear and consistent. Hence there has been a strong research effort directed at using formal logic as the basis for agent descriptions and agent implementation. Such a logical basis not only presents the clarity and consistency required but also allows for important techniques such as logical verification to be applied. We present a road map of research into the use of computational logic in agent‐based systems and survey much of the recent work in these areas. Even though, with such a rapidly changing field, it is impossible to cover every development, we aim to give the reader sufficient background to understand the current research problems and potential future developments in this maturing area.