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In this article we provide an analysis focusing on clinical use of two deep learning-based automatic detection tools in the field of radiology. The value of these technologies conceived to assist the physicians in the reading of imaging data (like X-rays) is generally assessed by the human-machine performance comparison, which does not take into account the complexity of the interpretation process of radiologists in its social, tacit and emotional dimensions. In this radiological vision work, data which informs the physician about the context surrounding a visible anomaly are essential to the definition of its pathological nature. Likewise, experiential data resulting from the contextual tacit knowledge that regulates professional conduct allows for the assessment of an anomaly according to the radiologist's, and patient's, experience. These data, which remain excluded from artificial intelligence processing, question the gap between the norms incorporated by the machine and those leveraged in the daily work of radiologists. The possibility that automated detection may modify the incorporation or the exercise of tacit knowledge raises questions about the impact of AI technologies on medical work. This article aims to highlight how the standards that emerge from the observation practices of radiologists challenge the automation of their vision work, but also under what conditions AI technologies are considered "objective" and trustworthy by professionals.

This paper examines a kind of explainable AI, centered around what we term pro-hoc explanations, that is a form of support that consists of offering alternative explanations (one for each possible outcome) instead of a specific post-hoc explanation following specific advice. Specifically, our support mechanism utilizes explanations by examples, featuring analogous cases for each category in a binary setting. Pro-hoc explanations are an instance of what we called frictional AI, a general class of decision support aimed at achieving a useful compromise between the increase of decision effectiveness and the mitigation of cognitive risks, such as over-reliance, automation bias and deskilling. To illustrate an instance of frictional AI, we conducted an empirical user study to investigate its impact on the task of radiological detection of vertebral fractures in x-rays. Our study engaged 16 orthopedists in a ‘human-first, second-opinion’ interaction protocol. In this protocol, clinicians first made initial assessments of the x-rays without AI assistance and then provided their final diagnosis after considering the pro-hoc explanations. Our findings indicate that physicians, particularly those with less experience, perceived pro-hoc XAI support as significantly beneficial, even though it did not notably enhance their diagnostic accuracy. However, their increased confidence in final diagnoses suggests a positive overall impact. Given the promisingly high effect size observed, our results advocate for further research into pro-hoc explanations specifically, and into the broader concept of frictional AI.

This paper discusses the results of an exploratory study aimed at investigating the impact of conversational agents (CAs) and specifically their agential characteristics on collaborative decision-making processes. The study involved 29 participants divided into 8 small teams engaged in a question-and-answer trivia-style game with the support of a text-based CA, characterized by two independent binary variables: personality (gentle and cooperative vs blunt and uncooperative) and gender (female vs male). A semi-structured group interview was conducted at the end of the experimental sessions to investigate the perceived utility and level of satisfaction with the CAs. Our results show that when users interact with a gentle and cooperative CA, their user satisfaction is higher. Furthermore, female CAs are perceived as more useful and satisfying to interact with than male CAs. We show that group performance improves through interaction with the CAs, confirming that a stereotype favoring the female with a gentle and cooperative personality combination exists in regard to perceived satisfaction, even though this does not lead to greater perceived utility. Our study extends the current debate about the possible correlation between CA characteristics and human acceptance and suggests future research to investigate the role of gender bias and related biases in human-AI teaming.

This demo showcases the DSS Quality Assessment Tool, an online service developed at the MUDI Lab (University of Milano-Bicocca, Milan, Italy) for the holistic assessment of the quality of AI-based decision support systems (DSS), along four quality dimensions: data similarity, calibration, robustness, and human interaction.

Augmented Intelligence (AuI) refers to the use of artificial intelligence (AI) to amplify certain cognitive tasks performed by human decision-makers. However, there are concerns that AI’s increasing capability and alignment with human values may undermine user agency, autonomy, and responsible decision-making. To address these concerns, we conducted a user study in the field of orthopedic radiology diagnosis, introducing a reflective XAI (explainable AI) support that aimed to stimulate human reflection, and we evaluated its impact of in terms of decision performance, decision confidence and perceived utility. Specifically, the reflective XAI support system prompted users to reflect on the dependability of AI-generated advice by presenting evidence both in favor of and against its recommendation. This evidence was presented via two cases that closely resembled a given base case, along with pixel attribution maps. These cases were associated with the same AI advice for the base case, but one case was accurate while the other was erroneous with respect to the ground truth. While the introduction of this support system did not significantly enhance diagnostic accuracy, it was highly valued by more experienced users. Based on the findings of this study, we advocate for further research to validate the potential of reflective XAI in fostering more informed and responsible decision-making, ultimately preserving human agency.

A comprehensive assessment of the impact of eXplainable AI (XAI) on diagnostic decision-making should adopt a socio-technical perspective. Our study focuses on Decision Support Systems (DSS) that provide explanations in the form of Activation Maps, assessing their impact in terms of automation bias and algorithmic aversion. Specifically, we focus on the XAI-assisted task of detecting thoraco-lumbar fractures from X-rays by radiologists, taking into account the complexity of the cases and the experience level of users. Our results show how XAI support has a clear and positive impact on diagnostic performance. By introducing the concepts of technology impact, reliance patterns, and the white box paradox, we highlight the importance of designing Human-AI Collaboration Protocols (HAI-CP) that are specific to the task at hand to optimize the integration of XAI into diagnostic decision-making.

The emergence of black-box, subsymbolic, and statistical AI systems has motivated a rapid increase in the interest regarding explainable AI (XAI), which encompasses both inherently explainable techniques, as well as approaches to make black-box AI systems explainable to human decision makers. Rather than always making black boxes transparent, these approaches are at risk of painting the black boxes white, thus failing to provide a level of transparency that would increase the system’s usability and comprehensibility, or even at risk of generating new errors (i.e., white-box paradox). To address these usability-related issues, in this work we focus on the cognitive dimension of users’ perception of explanations and XAI systems. We investigated these perceptions in light of their relationship with users’ characteristics (e.g., expertise) through a questionnaire-based user study involved 44 cardiology residents and specialists in an AI-supported ECG reading task. Our results point to the relevance and correlation of the dimensions of trust, perceived quality of explanations, and tendency to defer the decision process to automation (i.e., technology dominance). This contribution calls for the evaluation of AI-based support systems from a human–AI interaction-oriented perspective, laying the ground for further investigation of XAI and its effects on decision making and user experience.

In this paper, we present an exploratory study on the potential impact of holographic heart models and mixed reality technology on medical training, and in particular in teaching complex Congenital Heart Diseases (CHD) to medical students. Fifty-nine medical students were randomly allocated into three groups. Each participant in each group received a 30-minute lecture on a CHD condition interpretation and transcatheter treatment with different instructional tools. The participants of the first group attended a lecture in which traditional slides were projected onto a flat screen (group “regular slideware”, RS). The second group was shown slides incorporating videos of holographic anatomical models (group “holographic videos”, HV). Finally, those in the third group wore immersive, head-mounted devices (HMD) to interact directly with holographic anatomical models (group “mixed reality”, MR). At the end of the lecture, the members of each group were asked to fill in a multiple-choice questionnaire aimed at evaluating their topic proficiency, as a proxy to evaluate the effectiveness of the training session (in terms of acquired notions); participants from group MR were also asked to fill in a questionnaire regarding the recommendability and usability of the MS Hololens HMDs, as a proxy of satisfaction regarding its use experience (UX). The findings show promising results for usability and user acceptance.

In this paper, we present a fundamental framework for defining different types of explanations of AI systems and the criteria for evaluating their quality. Starting from a structural view of how explanations can be constructed, i.e., in terms of an explanandum (what needs to be explained), multiple explanantia (explanations, clues, or parts of information that explain), and a relationship linking explanandum and explanantia, we propose an explanandum-based typology and point to other possible typologies based on how explanantia are presented and how they relate to explanandia. We also highlight two broad and complementary perspectives for defining possible quality criteria for assessing explainability: epistemological and psychological (cognitive). These definition attempts aim to support the three main functions that we believe should attract the interest and further research of XAI scholars: clear inventories, clear verification criteria, and clear validation methods.

This paper deals with mineralogical-petrographic, microstructural and geochemical analyses for the archaeometric characterization of silicate-bearing granoblastic marbles from the so-called Waldensian Valleys of Piedmont (i.e. Pellice, Germanasca and lower Chisone valleys, Cottian Alps, Italy). These stones, belonging to the Dora-Maira Massif of the Penninic domain (pre-Carboniferous), are often associated with a talc-bearing mineralization. Since the XIV century, “Waldensian” marbles have been subjected to a relatively intense extractive activity (nowadays almost completely abandoned) and have been widely used, especially during XVI-XIX century, for decoration purposes and as building materials in the Duchy of Savoy, as well as later, in Italy and abroad. Fifteen samples representing four lithofacies and commercial types (named “Statuary white”, “Bardiglio gray”, “Zebrato gray” and “Cipollino green” marbles) from four different quarries (named Cabitto, La Majera, Rocca Corba, and Caugis), were studied through a multianalytical approach. The isotopic signature, combined with the mineral chemistry, allowed tracing marbles origin uniquely. The resulting database can be employed either in the cultural heritage, or in the conservation and restoration science, to identify each “Waldensian” marble from different decorative stones with similar microstructural and/or compositional features, but different origin.

In this article, we propose a conceptual and methodological framework for measuring the impact of the introduction of AI systems in decision settings, based on the concept of technological dominance, i.e. the influence that an AI system can exert on human judgment and decisions. We distinguish between a negative component of dominance (automation bias) and a positive one (algorithm appreciation) by focusing on and systematizing the patterns of interaction between human judgment and AI support, or reliance patterns, and their associated cognitive effects. We then define statistical approaches for measuring these dimensions of dominance, as well as corresponding qualitative visualizations. By reporting about four medical case studies, we illustrate how the proposed methods can be used to inform assessments of dominance and of related cognitive biases in real-world settings. Our study lays the groundwork for future investigations into the effects of introducing AI support into naturalistic and collaborative decision-making.

My research proposal explores the frictional design approach to stimulate cognitive engagement in Decision Support Systems, especially so for those that adopt eXplainable AI techniques (XAI). The aim of these programmed inefficiencies is the mitigation of human overreliance, while promoting thoughtfulness and cognitive enhancement, and ultimately improving the effectiveness of decision-making. Specifically, I will explore how frictional design principles can be applied to the development of AI decision support systems in order to create interfaces that encourage users to engage with the system actively and thoughtfully, rather than passively accepting its recommendations, in the form of cautious, comparative, judicial or adjunct support. By increasing transparency and promoting cognitive engagement, frictional design can help ensure that XAI decision support systems remain valuable tools for decision-makers, rather than becoming a crutch or a source of manipulation.

In this paper, we consider some key characteristics that AI should exhibit to enable hybrid agencies that include subject-matter experts and their AI-enabled decision aids. We will hint at the design requirements of guaranteeing that AI tools are: open, multiple, continuous, cautious, vague, analogical and, most importantly, adjunct with respect to decision-making practices. We will argue that especially adjunction is an important condition to design for. Adjunction entails the design and evaluation of human-AI interaction protocols aimed at improving AI usability, while also guaranteeing user satisfaction and human and social sustainability. It does so by boosting people's cognitive motivation for interacting analytically with the outputs, reducing overreliance on AI and improving performance.

La crisi climatica è innanzitutto una grande questione culturale, di cambiamento di paradigmi e di immaginario; a sua volta l’effetto climalterante dell’economia del carbonio staglia come un grande fallimento dell’immaginario culturale (Ghosh, 2016), con cui abbiamo pensato-o reso impensabili- le nostre relazioni ambientali. Siamo tutti partecipi di questa vertigine di fronte ad oggetti troppo ampli o planetari per essere contenuti, troppo tecnici per essere sociali, troppo lontani nello spazio e nel tempo per sentirli vicini o agire il cambiamento sociale, troppo spaventosi per renderli condivisibili e generativi nello spazio pubblico. Ciò anche perché gli stessi steccati disciplinari saltano di fronte ad eventi ambientali così intrecciati all’economia del carbonio (Mitchell, 2011), tanto più che la realtà non è divisa, tanto meno oggi, negli stessi confini. Proprio per districare il groviglio di emozioni, iperoggetti e soggetti di studio compresi sotto cambiamenti climatici, è necessario discernere (il significato originario di krino, crisi) alcune dinamiche cruciali che fanno della crisi climatica un’intima e intensa questione culturale. Innanzitutto, la causa principale sono le dimensioni tutte culturali del carbonio rilasciato in atmosfera, che hanno una loro storia capitalistica e dell’economia del carbonio, una loro idea di natura costruita socialmente, una loro mondanità nelle culture del consumo ad alto consumo energetico. In secondo luogo, i cambiamenti climatici (nel testo CC) rappresentano un amplificatore di dinamiche tutte sociali e culturali del processo di globalizzazione: esasperano i processi di marginalità, di perdita delle economie locali e comunità morali, di migrazioni, di ingiustizia, di impoverimento estremo e di competizione per le risorse presso tante popolazioni. In terzo luogo, il clima che cambia è culturale perché interpella e fa tremare le nostre scale di tempo e di spazio, apre a nuovi scenari connessi ad un’accelerazione

The overburning test method was developed to simulate and predict lime densification, agglomeration, and sporadic melting at the highest potential temperature of lime kilns. These events, that belong to the so-called ‘‘sticking tendency” phenomenon, are associated to formation of crusts, occlusions, and blockages in vertical lime kilns. In this work, fifty carbonate rocks, their insoluble residues, and derived burnt products were investigated through a multianalytical approach. Four groups of rocks having different burnability were identified. In addition, three fundamental mechanisms were related to the abovementioned phenomena: dehydration melting, solid-state, and liquid-phase sintering processes.