Andrea Esposito

Giuseppe Desolda, Andrea Esposito, Florian Müller et al.

Manufacturing tools like 3D printers have become accessible to the wider society, making the promise of digital fabrication for everyone seemingly reachable. While the actual manufacturing process is largely automated today, users still require knowledge of complex design applications to produce ready-designed objects and adapt them to their needs or design new objects from scratch. To lower the barrier to the design and customization of personalized 3D models, we explored novice mental models in voice-based 3D modeling by conducting a high-fidelity Wizard of Oz study with 22 participants. We performed a thematic analysis of the collected data to understand how the mental model of novices translates into voice-based 3D modeling. We conclude with design implications for voice assistants. For example, they have to: deal with vague, incomplete and wrong commands; provide a set of straightforward commands to shape simple and composite objects; and offer different strategies to select 3D objects.

Andrea Esposito, Alessandro Aldini, Marco Bernardo

The theory of noninterference supports the analysis of secure computations in multi-level security systems. Classical equivalence-based approaches to noninterference mainly rely on bisimilarity. In a nondeterministic setting, assessing noninterference through weak bisimilarity is adequate for irreversible systems, whereas for reversible ones branching bisimilarity has been recently proven to be more appropriate. In this paper we address the same two families of systems with the difference that probabilities come into play in addition to nondeterminism according to the alternating model of Hansson and Jonsson. For irreversible systems we extend the results of Aldini, Bravetti, and Gorrieri developed in a generative-reactive probabilistic setting, while for reversible systems we extend the results of Esposito, Aldini, Bernardo, and Rossi developed in a purely nondeterministic setting. We recast noninterference properties by adopting probabilistic variants of weak and branching bisimilarities for irreversible and reversible systems, respectively. Then we investigate a taxonomy of those properties as well as their preservation and compositionality aspects, along with a comparison with earlier taxonomies. The adequacy of the extended noninterference theory is illustrated via a probabilistic smart contract lottery.

Andrea Esposito, Miriana Calvano, Antonio Curci et al.

In recent years, Artificial Intelligence has become more and more relevant in our society. Creating AI systems is almost always the prerogative of IT and AI experts. However, users may need to create intelligent solutions tailored to their specific needs. In this way, AI systems can be enhanced if new approaches are devised to allow non-technical users to be directly involved in the definition and personalization of AI technologies. End-User Development (EUD) can provide a solution to these problems, allowing people to create, customize, or adapt AI-based systems to their own needs. This paper presents a systematic literature review that aims to shed the light on the current landscape of EUD for AI systems, i.e., how users, even without skills in AI and/or programming, can customize the AI behavior to their needs. This study also discusses the current challenges of EUD for AI, the potential benefits, and the future implications of integrating EUD into the overall AI development process.

Eleonora Cappuccio, Andrea Esposito, Francesco Greco et al.

Artificial Intelligence (AI) is one of the major technological advancements of this century, bearing incredible potential for users through AI-powered applications and tools in numerous domains. Being often black-box (i.e., its decision-making process is unintelligible), developers typically resort to eXplainable Artificial Intelligence (XAI) techniques to interpret the behaviour of AI models to produce systems that are transparent, fair, reliable, and trustworthy. However, presenting explanations to the user is not trivial and is often left as a secondary aspect of the system's design process, leading to AI systems that are not useful to end-users. This paper presents a Systematic Literature Review on Explanation User Interfaces (XUIs) to gain a deeper understanding of the solutions and design guidelines employed in the academic literature to effectively present explanations to users. To improve the contribution and real-world impact of this survey, we also present a platform to support Human-cEnteRed developMent of Explainable user interfaceS (HERMES) and guide practitioners and scholars in the design and evaluation of XUIs.

Marco Bernardo, Federico Calandra, Andrea Esposito et al.

Information and communication technologies are by now employed in most human activities, including economics and finance. Modern computers have reached an extraordinary power in terms of information processing, storage, retrieval, and transmission. However, several results of theoretical computer science imply the impossibility of certifying software quality in general. With the exception of safety-critical systems, this has primarily concerned information processed by confined systems, with limited socio-economic consequences. In the emerging era of technologies for exchanging tokenized assets and digital money over the Internet, such as in particular central bank digital currency (CBDC), even a minor bug could trigger a financial collapse. Although the aforementioned impossibility results cannot be overcome in an absolute sense, there exist formal methods that can provide correctness assertions for software system models under suitable conditions. We advocate their use to validate the operational resilience of software infrastructures enabling CBDC, with special emphasis on offline payments as they constitute a very critical issue.

Antonio Curci, Andrea Esposito

Tumors can manifest in various forms and in different areas of the human body. Brain tumors are specifically hard to diagnose and treat because of the complexity of the organ in which they develop. Detecting them in time can lower the chances of death and facilitate the therapy process for patients. The use of Artificial Intelligence (AI) and, more specifically, deep learning, has the potential to significantly reduce costs in terms of time and resources for the discovery and identification of tumors from images obtained through imaging techniques. This research work aims to assess the performance of a multimodal model for the classification of Magnetic Resonance Imaging (MRI) scans processed as grayscale images. The results are promising, and in line with similar works, as the model reaches an accuracy of around 98\%. We also highlight the need for explainability and transparency to ensure human control and safety.

Miriana Calvano, Antonio Curci, Giuseppe Desolda et al.

Artificial Intelligence (AI) spreads quickly as new technologies and services take over modern society. The need to regulate AI design, development, and use is strictly necessary to avoid unethical and potentially dangerous consequences to humans. The European Union (EU) has released a new legal framework, the AI Act, to regulate AI by undertaking a risk-based approach to safeguard humans during interaction. At the same time, researchers offer a new perspective on AI systems, commonly known as Human-Centred AI (HCAI), highlighting the need for a human-centred approach to their design. In this context, Symbiotic AI (a subtype of HCAI) promises to enhance human capabilities through a deeper and continuous collaboration between human intelligence and AI. This article presents the results of a Systematic Literature Review (SLR) that aims to identify principles that characterise the design and development of Symbiotic AI systems while considering humans as the core of the process. Through content analysis, four principles emerged from the review that must be applied to create Human-Centred AI systems that can establish a symbiotic relationship with humans. In addition, current trends and challenges were defined to indicate open questions that may guide future research for the development of SAI systems that comply with the AI Act.

Andrea Esposito, Miriana Calvano, Antonio Curci et al.

The integration of Artificial Intelligence (AI) in modern society is transforming how individuals perform tasks. In high-risk domains, ensuring human control over AI systems remains a key design challenge. This article presents a novel End-User Development (EUD) approach for black-box AI models, enabling users to edit explanations and influence future predictions through targeted interventions. By combining explainability, user control, and model adaptability, the proposed method advances Human-Centered AI (HCAI), promoting a symbiotic relationship between humans and adaptive, user-tailored AI systems.