En este texto, reflexiono sobre la evolución, las capacidades y los desafíos éticos de la inteligenciaartificial. Explico cómo la IA ha evolucionado desde ser definida como la creación de máquinas inteligentes hastaconvertirse en un ente jurídico que incluye técnicas como el aprendizaje automático, la lógica y la optimización.Repaso hitos históricos clave, desde Ramon Llull y Alan Turing hasta sistemas modernos como Deep Blue, Watson,AlphaGo y ChatGPT. Si bien los avances recientes son notables, también plantean preocupaciones éticas como ladesinformación, los sesgos y el uso irresponsable de la tecnología. Sostengo que los sistemas actuales de IA carecende razonamiento verdadero y comprensión moral, lo que hace esencial una reflexión colectiva sobre su desarrollo yaplicación. Propongo establecer un contrato social entre la tecnología y las comunidades humanas para garantizar quela IA evolucione dentro de marcos éticos sólidos y en armonía con los valores humanos.

While consciousness has been historically a heavily debated topic, awareness had less success in raising the interest of scholars. However, more and more researchers are getting interested in answering questions concerning what awareness is and how it can be artificially generated. The landscape is rapidly evolving, with multiple voices and interpretations of the concept being conceived and techniques being developed. The goal of this paper is to summarize and discuss the ones among these voices connected with projects funded by the EIC Pathfinder Challenge “Awareness Inside” callwithin Horizon Europe, designed specifically for fostering research on natural and synthetic awareness. In this perspective, we dedicate special attention to challenges and promises of applying synthetic awareness in robotics, as the development of mature techniques in this new field is expected to have a special impact on generating more capable and trustworthy embodied systems.

The topic of value alignment in AI has been gaining significant attention. The ultimate objective is how AI systems can align with human values. One of the main challenges, however, is identifying the relevant values. Some emerging works are focusing on learning relevant values through analysing our statements on social media. In most of these cases, learned values are simply specified through a label such as equality, fairness, or justice. However, the semantics of these values is not studied further. Addressing this gap, we propose a user study that provides us with a systematic approach for learning value semantics. The study is in the context of healthcare and the values of interest are the four fundamental bioethical principles: beneficence, non-maleficence, autonomy, and justice. We conduct a user study to collect the views of medical professionals on the value alignment of synthetic patient cases. We then search the space of candidate functions to find the one that best fits the participants answers, and hence, best describes their view of the value in question.

Humans possess innate collaborative capacities. However, effective teamwork often remains challenging. This study delves into the feasibility of collaboration within teams of rational, self-interested agents who engage in teamwork without the obligation to contribute. Drawing from psychological and game theoretical frameworks, we formalise teamwork as a one-shot aggregative game, integrating insights from Steiner's theory of group productivity. We characterise this novel game's Nash equilibria and propose a multiagent multi-armed bandit system that learns to converge to approximations of such equilibria. Our research contributes value to the areas of game theory and multiagent systems, paving the way for a better understanding of voluntary collaborative dynamics. We examine how team heterogeneity, task typology, and assessment difficulty influence agents' strategies and resulting teamwork outcomes. Finally, we empirically study the behaviour of work teams under incentive systems that defy analytical treatment. Our agents demonstrate human-like behaviour patterns, corroborating findings from social psychology research.

Clinical protocols are of great use in all medical fields, but their design and evaluation is complex and time-consuming. One of their major complexities is that they need to respect all bioethical values. For those reasons, in this paper, we address the problem of automating the design of clinical protocols that are in alignment with bioethical values. Following the AI alignment literature, we propose an algorithm to design value-aligned protocols in several steps: BPR (Bioethical Protocol Recommender). First, BPR learns the implicit bioethical value definitions of clinicians. Thereafter, BPR can apply these definitions to evaluate and compare potential actions for any given patient. With that, BPR builds a protocol that recommend those actions with maximum alignment with respect to all bioethical values, applying multi-objective optimisation techniques.

While personalisation in Human-Robot Interaction (HRI) has advanced significantly, most existing approaches focus on single-user adaptation, overlooking scenarios involving multiple stakeholders with potentially conflicting preferences. To address this, we propose the Multi-User Preferences Quantitative Bipolar Argumentation Framework (MUP-QBAF), a novel multi-user personalisation framework based on Quantitative Bipolar Argumentation Frameworks (QBAFs) that explicitly models and resolves multi-user preference conflicts. Unlike prior work in Argumentation Frameworks, which typically assumes static inputs, our approach is tailored to robotics: it incorporates both users' arguments and the robot's dynamic observations of the environment, allowing the system to adapt over time and respond to changing contexts. Preferences, both positive and negative, are represented as arguments whose strength is recalculated iteratively based on new information. The framework's properties and capabilities are presented and validated through a realistic case study, where an assistive robot mediates between the conflicting preferences of a caregiver and a care recipient during a frailty assessment task. This evaluation further includes a sensitivity analysis of argument base scores, demonstrating how preference outcomes can be shaped by user input and contextual observations. By offering a transparent, structured, and context-sensitive approach to resolving competing user preferences, this work advances the field of multi-user HRI. It provides a principled alternative to data-driven methods, enabling robots to navigate conflicts in real-world environments.

Social Media and the Internet have catalyzed an unprecedented potential for exposure to human diversity in terms of demographics, talents, opinions, knowledge, and the like. However, this potential has not come with new, much needed, instruments and skills to harness it. This paper presents our work on promoting richer and deeper social relations through the design and development of the "Internet of Us", an online platform that uses diversity-aware Artificial Intelligence to mediate and empower human social interactions. We discuss the multiple facets of diversity in social settings, the multidisciplinary work that is required to reap the benefits of diversity, and the vision for a diversity-aware hybrid human-AI society.

Humans possess innate collaborative capacities. However, effective teamwork often remains challenging. This study delves into the feasibility of collaboration within teams of rational, selfinterested agents who engage in teamwork without the obligation to contribute. Drawing from psychological and game theoretical frameworks, we formalise teamwork as a one-shot aggregative game, integrating insights from Steiner’s theory of group productivity. We characterise this novel game’s Nash equilibria and propose a multiagent multi-armed bandit system that learns to converge to approximations of such equilibria. Our research contributes value to the areas of game theory and multiagent systems, paving the way for a better understanding of voluntary collaborative dynamics. We examine how team heterogeneity, task typology, and assessment difficulty inuence agents’ strategies and resulting teamwork outcomes. Finally, we empirically study the behaviour of work teams under incentive systems that defy analytical treatment. Our agents demonstrate human-like behaviour patterns, corroborating ndings from social psychology research.

The approach of engaging students with real-world challenges to enhance collaboration and problem-solving has attracted significant interest from scholars and practitioners across diverse disciplines. Often called Challenge-Based Learning (CBL), this educational approach emphasises developing collaborative and problem-solving skills, with significant learning occurring within team settings. Prior studies highlight the influence of team composition on the efficacy of learning outcomes, pointing out that factors such as gender diversity, personality trait diversity, and a wide range of skills affect team dynamics and performance. Despite these insights, the practical organisation of these teams remains a challenge, often reliant on ad-hoc methods driven primarily by the nature of the setting at hand. Importantly, CBL is typically assessed through the final product, neglecting the impact of CBL on how the participants experience the overall process. That is, CBL is usually considered effective if the outcome is of high quality, ignoring participants' experience and participation quality. This study investigates the potential of an Artificial Intelligence team composition algorithm to improve participation quality and outcomes in collaborative CBL environments.

In many scenarios, the assessment by a single expert of all the content produced by an individual may be impractical due to the overall vast amount of content to be assessed by the expert. Examples are, for instance, online education services with thousands of students or scientific papers submitted to a conference that have to be assessed by program chairs in a short time period. Leveraging peer evaluations is a crucial strategy to mitigate assessment burdens and reduce the time required to deliver the expected results. This paper revisits the foundational concept of Personalised Automated Assessment (PAAS), which seeks to approximate the assessments of a particular community member, known as the leader, by integrating the peer assessments among the other community members of their answers to an assignment. Our extension of PAAS enhances its machine learning capabilities by integrating in the algorithm the semantic similarity among peer assessments to improve its prediction power. Experimental validation using synthetic and real-world datasets shows the efficacy of our extension, reducing prediction errors and increasing accuracy, especially in scenarios where the several assignments are significantly similar with one another.

The advent of autonomous vehicles heralds a new era in traffic management, presenting unprecedented opportunities and complex challenges. This paper aims to develop automated negotiation mechanisms for autonomous vehicles that navigate intersections without traditional traffic signals. We introduce a goal-oriented negotiation protocol grounded in the utilization of curvilinear coordinates. This approach is complemented by introducing a decision-making algorithm and a counteroffer algorithm for vehicles, both of which play pivotal roles in the negotiation protocol. Moreover, we provide evidence of the protocol's convergence and elucidate the time complexity of the underlying algorithm. We validate our algorithms with experiments using the AIM4 simulator, showcasing significant improvements in travel times compared to conventional traffic light systems and first-come-first-served methods. The results underscore our protocol's potential to reduce average travel time, enhancing overall traffic flow efficiency.

This paper is an extended abstract version of "Price of Anarchy of Traffic Assignment with Exponential Cost Functions[5]". We study a routing game where vehicles, selfish agents, independently choose routes to minimize travel delays from road congestion. We focus on exponential latency functions, unlike prior research using polynomial functions like BPR. We calculate a tight upper bound for the price of anarchy and compare it with the BPR function. Results indicate that the exponential function has a lower upper bound for traffic volumes below road capacity than the BPR function. Numerical analysis using real-world data shows that the exponential function closely approximates road latency with even tighter parameters, resulting in a relatively lower upper bound.

In the last decades, there has been a deceleration in the rates of poverty reduction, suggesting that traditional redistributive approaches to poverty mitigation could be losing effectiveness, and alternative insights to advance the number one UN Sustainable Development Goal are required. The criminalization of poor people has been denounced by several NGOs, and an increasing number of voices suggest that discrimination against the poor (a phenomenon known as aporophobia) could be an impediment to mitigating poverty. In this paper, we present the novel Aporophobia Agent-Based Model (AABM) to provide evidence of the correlation between aporophobia and poverty computationally. We present our use case built with real-world demographic data and poverty-mitigation public policies (either enforced or under parliamentary discussion) for the city of Barcelona. We classify policies as discriminatory or non-discriminatory against the poor, with the support of specialized NGOs, and we observe the results in the AABM in terms of the impact on wealth inequality. The simulation provides evidence of the relationship between aporophobia and the increase of wealth inequality levels, paving the way for a new generation of poverty reduction policies that act on discrimination and tackle poverty as a societal problem (not only a problem of the poor).

One of the possible ways to embed values into autonomous agents is through reasoning over the norms that govern the MAS where agents are situated. Unfortunately, most previous research on value alignment of norms does not take into consideration the strong social dimension of values. Here, we take the stance that agents should be able to reason not exclusively about their own values, but also take into account the values that others in their community hold and how they interpret them. In this work, we present a novel functionality for autonomous agents to compute the perspective-dependent value alignment of norms. We build upon and integrate previous work on value representation, normative reasoning and Theory of Mind (the ability to perceive and interpret others in terms of their mental states, such as beliefs). This novel functionality enables an agent to compute the alignment of a set of norms with respect to a set of values not exclusively from its opinion perspective, but to switch its value structure and perception of the world at run-time using Theory of Mind, to estimate the alignment that another agent may have for the same set of norms. Our proposal opens new grounds for research on value-based negotiation over normative systems, where agents can perform better if they can estimate the opinion that their interlocutors have on the proposals they make.

Recent advanced AI technologies, especially large language models (LLMs) like GPTs, have significantly advanced the field of data mining and led to the development of various LLM-based applications. AI for education (AI4EDU) is a vibrant multi-disciplinary field of data mining, machine learning, and education, with increasing importance and extraordinary potential. In this field, LLM and adaptive learning-based models can be utilized as interfaces in human-in-the-loop education systems, where the model serves as a mediator among the teacher, students, and machine capabilities, including its own. This perspective has several benefits, including the ability to personalize interactions, allow unprecedented flexibility and adaptivity for human-AI collaboration and improve the user experience. However, several challenges still exist, including the need for more robust and efficient algorithms, designing effective user interfaces, and ensuring ethical considerations are addressed. This workshop aims to bring together researchers and practitioners from academia and industry to explore cutting-edge AI technologies for personalized education, especially the potential of LLMs and adaptive learning technologies.

Theory of Mind capabilities, i.e. the capacity to adopt and reason from the perspective of others. By combining the Theory of Mind of TomAbd agents with abductive reasoning, agents can infer explanations for the behaviour of others, which they can incorporate into their own decision-making. We have implemented the TomAbd agent model and successfully tested its performance in the cooperative board game Hanabi.