One important evolution in software applications is the spread of service-oriented architectures in ubiquitous environments. Such environments are characterized by a wide set of interactive devices, with interactive applications that exploit a number of functionalities developed beforehand and encapsulated in Web services. In this article, we discuss how a novel model-based UIDL can provide useful support both at design and runtime for these types of applications. Web service annotations can also be exploited for providing hints for user interface development at design time. At runtime the language is exploited to support dynamic generation of user interfaces adapted to the different devices at hand during the user interface migration process, which is particularly important in ubiquitous environments.

In this paper, we propose UbiCicero, a multi-device, location-aware museum guide able to opportunistically\ud exploit large screens when users are nearby. Various types of games are included in addition to the\ud museum and artwork descriptions. The mobile guide is equipped with an RFID reader, which detects\ud nearby tagged artworks. By taking into account context-dependent information, including the current\ud user position and behaviour history, as well as the type of device available, more personalised and relevant\ud information is provided to the user, enabling a richer overall experience. We also present example\ud applications of this solution and then discuss the results of first empirical tests performed to evaluate the\ud usefulness and usability of the enhanced multi-device guid

Existing eXplainable Artificial Intelligence (XAI) techniques support people in interpreting AI advice. However, although previous work evaluates the users’ understanding of explanations, factors influencing the decision support are largely overlooked in the literature. This article addresses this gap by studying the impact of user uncertainty , AI correctness , and the interaction between AI uncertainty and explanation logic-styles for classification tasks. We conducted two separate studies: one requesting participants to recognize handwritten digits and one to classify the sentiment of reviews. To assess the decision making, we analyzed the task performance, agreement with the AI suggestion, and the user’s reliance on the XAI interface elements. Participants make their decision relying on three pieces of information in the XAI interface (image or text instance, AI prediction, and explanation). Participants were shown one explanation style (between-participants design) according to three styles of logical reasoning (inductive, deductive, and abductive). This allowed us to study how different levels of AI uncertainty influence the effectiveness of different explanation styles. The results show that user uncertainty and AI correctness on predictions significantly affected users’ classification decisions considering the analyzed metrics. In both domains (images and text), users relied mainly on the instance to decide. Users were usually overconfident about their choices, and this evidence was more pronounced for text. Furthermore, the inductive style explanations led to overreliance on the AI advice in both domains—it was the most persuasive, even when the AI was incorrect. The abductive and deductive styles have complex effects depending on the domain and the AI uncertainty levels.

A common criteria for Explainable AI (XAI) is to support users in establishing appropriate trust in the AI – rejecting advice when it is incorrect, and accepting advice when it is correct. Previous findings suggest that explanations can cause an over-reliance on AI (overly accepting advice). Explanations that evoke appropriate trust are even more challenging for decision-making tasks that are difficult for humans and AI. For this reason, we study decision-making by non-experts in the high-uncertainty domain of stock trading. We compare the effectiveness of three different explanation styles (influenced by inductive, abductive, and deductive reasoning) and the role of AI confidence in terms of a) the users’ reliance on the XAI interface elements (charts with indicators, AI prediction, explanation), b) the correctness of the decision (task performance), and c) the agreement with the AI’s prediction. In contrast to previous work, we look at interactions between different aspects of decision-making, including AI correctness, and the combined effects of AI confidence and explanations styles. Our results show that specific explanation styles (abductive and deductive) improve the user’s task performance in the case of high AI confidence compared to inductive explanations. In other words, these styles of explanations were able to invoke correct decisions (for both positive and negative decisions) when the system was certain. In such a condition, the agreement between the user’s decision and the AI prediction confirms this finding, highlighting a significant agreement increase when the AI is correct. This suggests that both explanation styles are suitable for evoking appropriate trust in a confident AI. Our findings further indicate a need to consider AI confidence as a criterion for including or excluding explanations from AI interfaces. In addition, this paper highlights the importance of carefully selecting an explanation style according to the characteristics of the task and data.