Enrique Herrera-Viedma

Andrés Herrera-Poyatos, Javier Del Ser, Marcos López de Prado et al.

Artificial intelligence (AI) has matured as a technology, necessitating the development of responsibility frameworks that are fair, inclusive, trustworthy, safe and secure, transparent, and accountable. By establishing such frameworks, we can harness the full potential of AI while mitigating its risks, particularly in high-risk scenarios. This requires the design of responsible AI systems based on trustworthy AI technologies and ethical principles, with the aim of ensuring auditability and accountability throughout their design, development, and deployment, adhering to domain-specific regulations and standards. This paper explores the concept of a responsible AI system from a holistic perspective, which encompasses four key dimensions: 1) regulatory context; 2) trustworthy AI technology along with standardization and assessments; 3) auditability and accountability; and 4) AI governance. The aim of this paper is double. First, we analyze and understand these four dimensions and their interconnections in the form of an analysis and overview. Second, the final goal of the paper is to propose a roadmap in the design of responsible AI systems, ensuring that they can gain society's trust. To achieve this trustworthiness, this paper also fosters interdisciplinary discussions on the ethical, legal, social, economic, and cultural aspects of AI from a global governance perspective. Last but not least, we also reflect on the current state and those aspects that need to be developed in the near future, as ten lessons learned.

Qianlei Jia, Xinliang Zhou, Ondrej Krejcar et al.

In group decision-making (GDM) scenarios, uncertainty, dynamic social structures, and vague information present major challenges for traditional opinion dynamics models. To address these issues, this study proposes a novel social network group decision-making (SNGDM) framework that integrates three-way decision (3WD) theory, dynamic network reconstruction, and linguistic opinion representation. First, the 3WD mechanism is introduced to explicitly model hesitation and ambiguity in agent judgments, thereby preventing irrational decisions. Second, a connection adjustment rule based on opinion similarity is developed, enabling agents to adaptively update their communication links and better reflect the evolving nature of social relationships. Third, linguistic terms are used to describe agent opinions, allowing the model to handle subjective, vague, or incomplete information more effectively. Finally, an integrated multi-agent decision-making framework is constructed, which simultaneously considers individual uncertainty, opinion evolution, and network dynamics. The proposed model is applied to a multi-UAV cooperative decision-making scenario, where simulation results and consensus analysis demonstrate its effectiveness. Experimental comparisons further verify the advantages of the algorithm in enhancing system stability and representing realistic decision-making behaviors.

Ruolan Cheng, Yong Deng, Enrique Herrera-Viedma

Random permutation set (RPS) is a new formalism for reasoning with uncertainty involving order information. Measuring the conflict between two pieces of evidence represented by permutation mass functions remains an urgent research topic in order-structured uncertain information fusion. In this paper, a detailed analysis of conflicts in RPS is carried out from two different perspectives: random finite set (RFS) and Dempster-Shafer theory (DST). Starting from the observation of permutations, we first define an inconsistency measure between permutations inspired by the rank-biased overlap(RBO) measure and further propose a non-overlap-based conflict measure method for RPSs. This paper regards RPS theory (RPST) as an extension of DST. The order information newly added in focal sets indicates qualitative propensity, characterized by top-ranked elements occupying a more critical position. Some numerical examples are used to demonstrate the behavior and properties of the proposed conflict measure. The proposed method not only has the natural top-weightedness property and can effectively measure the conflict between RPSs from the DST view but also provides decision-makers with a flexible selection of weights, parameters, and truncated depths.

Oier Mentxaka, Natalia Díaz-Rodríguez, Mark Coeckelbergh et al.

Artificial Intelligence (AI) poses both significant risks and valuable opportunities for democratic governance. This paper introduces a dual taxonomy to evaluate AI's complex relationship with democracy: the AI Risks to Democracy (AIRD) taxonomy, which identifies how AI can undermine core democratic principles such as autonomy, fairness, and trust; and the AI's Positive Contributions to Democracy (AIPD) taxonomy, which highlights AI's potential to enhance transparency, participation, efficiency, and evidence-based policymaking. Grounded in the European Union's approach to ethical AI governance, and particularly the seven Trustworthy AI requirements proposed by the European Commission's High-Level Expert Group on AI, each identified risk is aligned with mitigation strategies based on EU regulatory and normative frameworks. Our analysis underscores the transversal importance of transparency and societal well-being across all risk categories and offers a structured lens for aligning AI systems with democratic values. By integrating democratic theory with practical governance tools, this paper offers a normative and actionable framework to guide research, regulation, and institutional design to support trustworthy, democratic AI. It provides scholars with a conceptual foundation to evaluate the democratic implications of AI, equips policymakers with structured criteria for ethical oversight, and helps technologists align system design with democratic principles. In doing so, it bridges the gap between ethical aspirations and operational realities, laying the groundwork for more inclusive, accountable, and resilient democratic systems in the algorithmic age.

Natalia Díaz-Rodríguez, Javier Del Ser, Mark Coeckelbergh et al.

Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system's entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective. However, attaining truly trustworthy AI concerns a wider vision that comprises the trustworthiness of all processes and actors that are part of the system's life cycle, and considers previous aspects from different lenses. A more holistic vision contemplates four essential axes: the global principles for ethical use and development of AI-based systems, a philosophical take on AI ethics, a risk-based approach to AI regulation, and the mentioned pillars and requirements. The seven requirements (human agency and oversight; robustness and safety; privacy and data governance; transparency; diversity, non-discrimination and fairness; societal and environmental wellbeing; and accountability) are analyzed from a triple perspective: What each requirement for trustworthy AI is, Why it is needed, and How each requirement can be implemented in practice. On the other hand, a practical approach to implement trustworthy AI systems allows defining the concept of responsibility of AI-based systems facing the law, through a given auditing process. Therefore, a responsible AI system is the resulting notion we introduce in this work, and a concept of utmost necessity that can be realized through auditing processes, subject to the challenges posed by the use of regulatory sandboxes. Our multidisciplinary vision of trustworthy AI culminates in a debate on the diverging views published lately about the future of AI. Our reflections in this matter conclude that regulation is a key for reaching a consensus among these views, and that trustworthy and responsible AI systems will be crucial for the present and future of our society.

Jiehan Zhou, Shouhua Zhang, Qinghua Lu et al.

Federated learning (FL) brings collaborative intelligence into industries without centralized training data to accelerate the process of Industry 4.0 on the edge computing level. FL solves the dilemma in which enterprises wish to make the use of data intelligence with security concerns. To accelerate industrial Internet of things with the further leverage of FL, existing achievements on FL are developed from three aspects: 1) define terminologies and elaborate a general framework of FL for accommodating various scenarios; 2) discuss the state-of-the-art of FL on fundamental researches including data partitioning, privacy preservation, model optimization, local model transportation, personalization, motivation mechanism, platform & tools, and benchmark; 3) discuss the impacts of FL from the economic perspective. To attract more attention from industrial academia and practice, a FL-transformed manufacturing paradigm is presented, and future research directions of FL are given and possible immediate applications in Industry 4.0 domain are also proposed.

Cristina Zuheros, Eugenio Martínez-Cámara, Enrique Herrera-Viedma et al.

Decision making models are constrained by taking the expert evaluations with pre-defined numerical or linguistic terms. We claim that the use of sentiment analysis will allow decision making models to consider expert evaluations in natural language. Accordingly, we propose the Sentiment Analysis based Multi-person Multi-criteria Decision Making (SA-MpMcDM) methodology for smarter decision aid, which builds the expert evaluations from their natural language reviews, and even from their numerical ratings if they are available. The SA-MpMcDM methodology incorporates an end-to-end multi-task deep learning model for aspect based sentiment analysis, named DOC-ABSADeepL model, able to identify the aspect categories mentioned in an expert review, and to distill their opinions and criteria. The individual evaluations are aggregated via the procedure named criteria weighting through the attention of the experts. We evaluate the methodology in a case study of restaurant choice using TripAdvisor reviews, hence we build, manually annotate, and release the TripR-2020 dataset of restaurant reviews. We analyze the SA-MpMcDM methodology in different scenarios using and not using natural language and numerical evaluations. The analysis shows that the combination of both sources of information results in a higher quality preference vector.

Ivan Palomares, Carlos Porcel, Luiz Pizzato et al.

There exist situations of decision-making under information overload in the Internet, where people have an overwhelming number of available options to choose from, e.g. products to buy in an e-commerce site, or restaurants to visit in a large city. Recommender systems arose as a data-driven personalized decision support tool to assist users in these situations: they are able to process user-related data, filtering and recommending items based on the users preferences, needs and/or behaviour. Unlike most conventional recommender approaches where items are inanimate entities recommended to the users and success is solely determined upon the end users reaction to the recommendation(s) received, in a Reciprocal Recommender System (RRS) users become the item being recommended to other users. Hence, both the end user and the user being recommended should accept the 'matching' recommendation to yield a successful RRS performance. The operation of an RRS entails not only predicting accurate preference estimates upon user interaction data as classical recommenders do, but also calculating mutual compatibility between (pairs of) users, typically by applying fusion processes on unilateral user-to-user preference information. This paper presents a snapshot-style analysis of the extant literature that summarizes the state-of-the-art RRS research to date, focusing on the algorithms, fusion processes and fundamental characteristics of RRS, both inherited from conventional user-to-item recommendation models and those inherent to this emerging family of approaches. Representative RRS models are likewise highlighted. Following this, we discuss the challenges and opportunities for future research on RRSs, with special focus on (i) fusion strategies to account for reciprocity and (ii) emerging application domains related to social recommendation.

Simon James Fong, Gloria Li, Nilanjan Dey et al.

In the advent of the novel coronavirus epidemic since December 2019, governments and authorities have been struggling to make critical decisions under high uncertainty at their best efforts. Composite Monte-Carlo (CMC) simulation is a forecasting method which extrapolates available data which are broken down from multiple correlated/casual micro-data sources into many possible future outcomes by drawing random samples from some probability distributions. For instance, the overall trend and propagation of the infested cases in China are influenced by the temporal-spatial data of the nearby cities around the Wuhan city (where the virus is originated from), in terms of the population density, travel mobility, medical resources such as hospital beds and the timeliness of quarantine control in each city etc. Hence a CMC is reliable only up to the closeness of the underlying statistical distribution of a CMC, that is supposed to represent the behaviour of the future events, and the correctness of the composite data relationships. In this paper, a case study of using CMC that is enhanced by deep learning network and fuzzy rule induction for gaining better stochastic insights about the epidemic development is experimented. Instead of applying simplistic and uniform assumptions for a MC which is a common practice, a deep learning-based CMC is used in conjunction of fuzzy rule induction techniques. As a result, decision makers are benefited from a better fitted MC outputs complemented by min-max rules that foretell about the extreme ranges of future possibilities with respect to the epidemic.