Cristian Barrué

Explainability has been proposed as an approach to robot failure recovery, facilitating understanding and repairing trust, especially relevant in domestic assistive tasks. This study conducts a preliminary exploration of older adults' preferences regarding the content and context of robot-generated explanations for failures to guide future research. An exploratory study was conducted in three phases: 1) gathering high-level requirements from caregivers, 2) implementing a semi-autonomous robot for object retrieval that identifies and explains different types of failures, and 3) engaging N=8 older adults in real-life interactions as well as in focus groups to assess their perspectives. Our preliminary observations highlight a tension in preferences: a general desire for short, direct explanations to minimize disruption, versus a need for more detailed, actionable explanations specifically in failure cases. Crucially, we also note that these preferences are unstable and contextually constructed, reinforcing that the technical failures cannot be separated from their social context, as users' experiences and opinions are shaped by both the robot’s functional capabilities and the values and organisational settings in which they are introduced.

This paper explores the relationship between meal-assistance robots (MARs) and patient autonomy through a focused ethnography of a pilot experiment in a palliative care hospital. Drawing on the theoretical approach of Science and Technology Studies (STS), the study challenges the dominant dyadic model of human-robot interaction by proposing a relational framework that emphasizes the robot’s integration within the patient’s broader care network. While MARs currently have limited functionality for enabling autonomous eating in patients with severe mobility problems, participants reported enhanced well-being and expressed a strengthened sense of autonomy through their interactions with the robot. By integrating the notion of relational autonomy with a sociotechnical lens, the study demonstrates that the robot’s impact on autonomy is not solely a function of its technical capabilities but is co-produced within the care context. Central to these results is the notion that we call the robot’s heteronomy capacity —its ability to interact with and adapt to multiple agents and contexts. These findings contribute to a shift in the ontology of socially assistive robots (SARs), highlighting how robotic agency is dynamically produced through socially embedded interactions. The research underscores the importance of interdisciplinary approaches in MARs design and healthcare implementation, advocating for flexible systems that are open to relational heteronomy as a means of supporting patient autonomy and well-being.

This special issue follows the discussions held at the 2024 Edition of The Workshop on the History of Artificial Intelligence (WHAI 2024), celebrated in Santiago de Compostela within the framework of the 27th European Conference on Artificial Intelligence (ECAI 2024). In WHAI, presentations were held about the starting and evolution of artificial intelligence (AI) in several European regions, and about the contribution of European research and researchers to specific paradigms and to the history of AI in general. Following these discussions, this issue includes the extended versions of two papers presented at WHAI, one on how AI originated at the University of Edinburgh and another on the contributions of European researchers to the foundations and applications of model-based reasoning. The special issue is now enriched with a historical perspective of AI in Catalonia, the motivation for the AI community to build and disseminate the timeline of the History of AI in Europe and a proposal for a Collective Memory of Artificial Intelligence, which expands the timeline initiative. All papers are a contribution to The History of AI Timeline, an open project for the future, which also began with WHAI2024 and will be updated and enriched through the discussions of subsequent editions of WHAI.

As artificial intelligence (AI) and its applications continue to reshape our world, understanding its historical roots is important, and increasingly so, for several explicit reasons. First, the pace and scale of AI’s impact are accelerating. The rapid integration of AI into critical sectors—such as healthcare, transportation, finance, and education—means that decisions about research, policy, and regulation must be informed by a deep understanding of how current technologies evolved and the lessons learned from past successes and failures. Second, the importance of understanding AI’s history is growing because ethical, societal, and regulatory questions are becoming more urgent. This is particularly true for Europe, where a rich tapestry of AI-based research and innovation has unfolded over the past seven decades. We hypothesize that by documenting the histories of AI in Europe through this timeline, we will uncover underrepresented milestones, providing insights into future AI research directions and policy developments. This timeline will offer a comprehensive historical record and serve as a practical tool for analyzing trends in AI development specific to the European context.

Objectives: In the present study, we aimed (i) to describe the personality traits of a cohort of post-COVID-19 condition (PCC) patients compared with a healthy control (HC) group, (ii) to evaluate the relationship between sleep quality and personality traits, and (iii) to investigate whether this relationship differs according to disease severity. Methods: We included 599 participants from the Nautilus Project (ClincalTrials.gov IDs: NCT05307549 and NCT05307575) with an age range from 20 to 65 years old. Of 599 participants, 280 were nonhospitalized (mild PCC), 87 were hospitalized (hospitalized PCC), 98 were in the PCC-ICU, and 134 were in the HC group. We assessed sleep quality with the Pittsburgh Sleep Quality Index (PSQI) and personality traits with the NEO Five-Factor Inventory (NEO FFI). Results: We found that mild-PCC patients had higher scores of neuroticism than HCs (p < 0.001) and ICU-PCC patients did (p = 0.020). The higher the neuroticism score was, the higher the total PSQI score (B 0.162; p < 0.001), the worse the sleep latency (B 0.049; p < 0.001), the greater the degree of sleep disturbance (B 0.060; p < 0.001), the greater the use of sleeping medication (B 0.035; p = 0.033), and the greater the incidence of daytime disturbances (B 0.065; p < 0.001) among the PCC patients. High neuroticism is also an indicator of worse sleep quality in mild-PCC (t = 3.269; p 0.001) and hospitalized-PCC (t = 6.401; p < 0.001) patients and HCs (t = 4.876; p < 0.001) but not in ICU-PCC patients. Conclusions: Although neuroticism affected sleep quality in both the PCC patients and HCs, the clinical implications and magnitude of the relationship were more significant in the PCC group. Specific and multidimensional interventions are needed to treat sleep problems in this population, and the influence of their personality traits should be considered.

Background/Objectives: Studies using optical coherence tomography angiography (OCTA) have revealed that individuals recovering from COVID-19 have a reduced retinal vascular density (VD) and larger foveal avascular zones (FAZs) than healthy individuals, with more severe cases showing greater reductions. We aimed to examine aspects of the retinal microvascularization in patients with post-COVID-19 condition (PCC) classified by COVID-19 severity and how these aspects relate to cognitive performance. Methods: This observational cross-sectional study included 104 PCC participants from the NAUTILUS Project, divided into severe (n = 59) and mild (n = 45) COVID-19 groups. Participants underwent cognitive assessments and OCTA to measure VD and perfusion density (PD) in the superficial capillary plexus (SVP) and FAZ. Analysis of covariance and partial Pearson and Spearman correlations were used to study intergroup differences and the relationships between cognitive and OCTA variables. Results: Severe PCC participants had significantly lower central (p = 0.03) and total (p = 0.03) VD, lower central (p = 0.02) PD measurements, and larger FAZ areas (p = 0.02) and perimeters (p = 0.02) than mild cases. Severe cases showed more cognitive impairment, particularly in speed processing (p = 0.003) and executive functions (p = 0.03). Lower central VD, lower central PD, and larger FAZ areas and perimeters were associated with worse executive function performance in the entire PCC sample and in the mild COVID-19 group. Conclusions: Retinal microvascular alterations, characterized by reduced VD and PD in the SVP and larger FAZ areas, were associated with cognitive impairments in PCC individuals. These findings suggest that severe COVID-19 leads to long-lasting microvascular damage, impacting retinal and cognitive health.

This paper presents the results of an extensive study investigating the opinions on Artificial Intelligence (AI) of a sample of 4,006 European citizens from eight distinct countries (France, Germany, Italy, Netherlands, Poland, Romania, Spain, and Sweden). The aim of the study is to gain a better understanding of people’s views and perceptions within the European context, which is already marked by important policy actions and regulatory processes. To survey the perceptions of the citizens of Europe, we design and validate a new questionnaire (PAICE) structured around three dimensions: people’s awareness, attitude, and trust. We observe that while awareness is characterized by a low level of self-assessed competency, the attitude toward AI is very positive for more than half of the population. Reflecting on the collected results, we highlight implicit contradictions and identify trends that may interfere with the creation of an ecosystem of trust and the development of inclusive AI policies. The introduction of rules that ensure legal and ethical standards, along with the activity of high-level educational entities, and the promotion of AI literacy are identified as key factors in supporting a trustworthy AI ecosystem.We make some recommendations for AI governance focused on the European context and conclude with suggestions for future work.

In some contexts, like geriatric hospitals, the number of patients requiring assistance with feeding is very high and robots may be an effective tool for caregivers to provide better assistance. This article introduces NYAM, a robot designed to aid in the feeding process for individuals. Our robot is equipped with a mechanism to effectively recapture the person's attention whenever necessary. The mechanism is easily adjustable by the caregivers, allowing the straightforward customisation of the feeding service. The approach was evaluated, within a geriatric hospital, with 9 patients who used the robot for 5 consecutive days. We argue that incorporating enhanced social aspects into the robot is imperative to enhance the effectiveness and acceptance of this solution.

The advancements in human-centered artificial intelligence (HCAI) systems for Industry 5.0 is a new phase of industrialization that places the worker at the center of the production process and uses new technologies to increase prosperity beyond jobs and growth. HCAI presents new objectives that were unreachable by either humans or machines alone, but this also comes with a new set of challenges. Our proposed method accomplishes this through the knowlEdge architecture, which enables human operators to implement AI solutions using a zero-touch framework. It relies on containerized AI model training and execution, supported by a robust data pipeline and rounded off with human feedback and evaluation interfaces. The result is a platform built from a number of components, spanning all major areas of the AI lifecycle. We outline both the architectural concepts and implementation guidelines and explain how they advance HCAI systems and Industry 5.0. In this article, we address the problems we encountered while implementing the ideas within the edge-to-cloud continuum. Further improvements to our approach may enhance the use of AI in Industry 5.0 and strengthen trust in AI systems.