Pietro Baroni

Ransomware extortion is both a technical attack and a negotiation in which attackers seek payment and victims try to limit loss, gain time, and test the threat’s credibility. In this paper, we study publicly leaked ransomware negotiations as argumentative dialogues and analyse them through dialogue types, dialogue moves, and argumentation schemes. We use a fully automated, two-stage, LLM-based adjudicated annotation workflow for hostile conversational data, where inferential episodes often span several short messages and do not align with message boundaries. The analysis shows that ransomware negotiation is not mere coercion: negotiation dialogue and practical reasoning dominate, while value appeals, causal forecasts, evidential claims, and procedural manoeuvres also shape the exchange. Hence, ransomware negotiation can be effectively analysed with computational argumentation and such analysis requires methods designed for compressed, strategic, and coercive dialogue.

In order to provide personalized recommendations, decision support methods need to take into account user preferences in the production of their outcomes. This is a particularly relevant issue in the case of argumentative approaches to decision support, which have often focused on the construction of user-agnostic arguments concerning the various options at stake. In particular, while gradual bipolar argumentation (GBA) has been successfully adopted as a formal basis for the realization of decision support in a variety of application domains, none of these previous works involved personalization aspects and, hence, the study of techniques to integrate user-provided preferences into GBA turns out to be an open research question. In this paper, we provide an initial contribution to this investigative direction from both a theoretical and a practical perspective. On the theoretical side, we introduce a property of local coherence to characterize the expected effects of user preferences on argument strength assessment in GBA and provide results concerning the relations between local coherence and global behaviors. On the practical side, we illustrate a preliminary experimentation in the context of a GBA-based review aggregation system extended with the handling of user preferences, which allows us to draw some considerations on the opportunities and challenges of putting the proposed approach into practice.

Galenic formulations are personalized medicines prepared by pharmacists in their laborato ries. They are produced in small batches considering single patients’ characteristics, such as age, gender, allergies, and the like, thus contributing to responsible health. The production process is performed manually with the support of mechanic machines. This activity is time-consuming, prone to errors, and subject to quality variations. In this paper, we propose the integration of collaborative robots into the galenic formulation process to obtain several advantages, such as increased productivity, reduced variability, improved accuracy, and minimized risks associated with human error. Additionally, the use of robots can alleviate the physical burden on human operators, allowing them to focus on higher-level tasks that require critical thinking and decision-making. To achieve this goal, a software application, called PRAISE (Pharmaceutical Robotic and AI System for End users), has been developed; it is meant to support end users (i.e., pharmacists) in defining robot programs suitable to the case at hand. This application is conceived as an End-User Development (EUD) environment, which implements a hybrid interaction approach based on a natural language interface leveraging Large Language Models and a graphical interface to check and possibly revise the user-created robot programs. A user study carried out with nine pharmacists demonstrates the validity of the approach.

Galenic preparations are patient-centered medicines prepared by pharmacists or veterinarians, which allow personalizing dosages, overcoming allergy problems, reducing costs, and dealing with rare diseases. However, the current manual production process of galenic preparations poses several challenges to human workers. This paper proposes the use of collaborative robots to help pharmacists carry out the most tiresome, precise, error-prone, and time-consuming tasks. In particular, an end-user development (EUD) environment called PRAISE (pharmaceutical robotic and AI system for end users) has been designed to support pharmacists in programming the tasks to be performed by a collaborative robot. The EUD environment integrates artificial intelligence (AI) features based on large language models but ensures that end users always have complete control over the generated output, that is, a robot program. The paper focuses on the application of a human-centered methodology adopted to design PRAISE by involving representative end users (experts in the pharmaceutical sector) from system ideation to its evaluation. Design implications related to AI-enabled EUD for collaborative robots are the main findings of the paper.

As medication adherence represents a critical challenge in healthcare, pill and medication dispensers have gained increasing attention as potential solutions to promote adherence and improve patient outcomes. Following the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) methodology, we carried out a systematic literature review on papers indexed in Scopus and PubMed, which present solutions for pill or medication dispensers. Given the importance of user acceptance for these solutions, the research questions of the survey are driven by a human-centered perspective. We first provide an overview of the different solutions, classifying them according to their stage of development. We then analyze each solution considering its hardware/software architecture. Finally, we review the characteristics of user interfaces designed for interacting with pill and medication dispensers and analyze the involvement of different types of users in dispenser management. On the basis of this analysis, we draw findings and indications for future research that are aimed to provide insights to healthcare professionals, researchers, and designers who are interested in developing and using pill and medication dispensers.

A user receiving an explanation for outcomes produced by an artificially intelligent system expects that it satisfies the key property of descriptive accuracy (DA), i.e. that the explanation contents are in correspondence with the internal working of the system. Crucial as this property appears to be, it has been somehow overlooked in the XAI literature to date. To address this problem, we consider the questions of formalising DA and of analysing its satisfaction by explanation methods. We provide formal definitions of naive, structural and dialectical DA, using the family of probabilistic classifiers as the context for our analysis. We evaluate the satisfaction of our given notions of DA by several explanation methods, amounting to two popular feature-attribution methods from the literature and a novel form of explanation that we propose and complement our analysis with experiments carried out on a varied selection of concrete probabilistic classifiers.

We propose a generic notion of consistency in an abstract labelling setting, based on two relations: one of intolerance between the labelled elements and one of incompatibility between the labels assigned to them, thus allowing a spectrum of consistency requirements depending on the actual choice of these relations. As a first application to formal argumentation, we show that traditional Dung’s semantics can be put in correspondence with different consistency requirements in this context. We consider then the issue of consistency preservation when a labelling is obtained as a synthesis of a set of labellings, as is the case for the traditional notion of argument justification. In this context we provide a general characterization of consistency-preserving synthesis functions and analyze the case of argument justification in this respect.