Albert Sabater

Informe sobre el Model PIO Empresa. El present document està estructurat en una primera secció sobre la necessitat d’un Model PIO específic en l’àmbit de les pimes que implementen un sistema d’IA, seguida del context i l’ecosistema actual d’aquests agents. Tot seguit, s’explica quins són els subjectes als quals està dirigit el Model PIO Empresa. A continuació, es determina la capacitació i l’alfabetització, com també l’estructura de la intel·ligència artificial responsable. En la següent secció es detallen la justificació legal, de governança i de sostenibilitat. Les anomenades “preguntes prèvies” son un recull de 5 interrogacions inicials que s’han de fer abans de començar l'autoavaluació. Seguidament, es mostren les preguntes que comprenen el qüestionari del Model PIO Empresa. A més, es presenta una guia estratègica per aquelles pimes que volen implementar un sistema d’IA de manera responsable però tenen pocs mitjans per desenvolupar les exigències del qüestionari. Finalment, es preveu un qüestionari per seleccionar al personal proveïdor de sistemes d’IA.

This lecture examines the impact of artificial intelligence (AI) in higher education from an ethical and responsible use perspective. It argues that AI is not a new phenomenon, but its recent popularisation—particularly through large language models—demands a clear educational and institutional response. The risks of anthropomorphising AI tools, technological over-reliance, loss of critical thinking, and environmental and social unsustainability are discussed. At the same time, benefits such as creative exploration, multiplication of pedagogical examples, and error detection are acknowledged. The lecture proposes a responsible AI model built around seven principles—transparency, fairness, accountability, security, privacy, autonomy, and sustainability—and presents an evaluation tool developed by the Ethics in Artificial Intelligence Observatory of Catalonia to help institutions assess and improve their ethical use of AI. Keywords: artificial intelligence, ethics, transparency, higher education, responsibility, sustainability, critical thinking, large language models, digital literacy, responsible AI

This chapter examines the development and application of AI in social services through stakeholder engagement within the AI FORA project, drawing on meetings held in May 2022 and 2023 with diverse participants, including marginalized communities, social workers, policymakers, technologists, and academics. Focusing on Spain as a case study, we identify key challenges and opportunities in AI-driven social service provision, emphasizing the importance of compositional (demographic and socioeconomic factors), contextual (local systems and biases), and collective (community trust and participation) dimensions. Our findings reveal that AI risks exacerbating inequities when these factors are overlooked. However, stakeholders pointed out that context-aware AI applications, designed with adaptability, transparency, and participatory oversight can mitigate these risks. The chapter highlights three key policy lessons: (1) AI systems must evolve through continuous auditing and community input, (2) predictive tools should account for local realities to avoid bias, and (3) participatory governance is essential to ensure equity.

This chapter provides a meta-level self-reflection of research on AI-based social assessment with regard to its handling of normativity. It is motivated by the idea that, on the one hand, there cannot be a one-fits-all solution to ethical issues due to the high diversity of cultures and contexts. On the other hand, it is necessary to discuss the possibility of a common position based on shared fundamental norms such as non-discrimination and fairness. These efforts, reflected by the idea of establishing an Ethical Observatory, should be relevant to any research on AI-based social assessment. This chapter, therefore, explores various ethical issues related to AI in the context of social service provision, linking them to a reflective approach to social justice and how it could or should be improved by better AI. As different cultural values claim moral validity, it is necessary to scrutinise the relationship between their contextuality and universality. On this basis, discourse ethics is proposed as a model for dealing with cultural differences without abandoning universally valid standards, linking it with the role of safe spaces and stakeholder dialogues. Furthermore, it shows how the justification of moral norms can be reasonably argued. Based on this, important research ethical aspects will be considered.

After reviewing some experiences of AI use in social services in Catalonia due to its key role in developing the first AI strategy in Spain, our work adopts an ethics from below perspective through Participatory Action Research (PAR), engaging social workers, policymakers, technologists, developers, and social scientists to assess AI-based social services in Catalonia. Two key meetings under the umbrella of the AI FORA project illuminated the discourse, highlighting the importance of continuous, inclusive evaluation to maintain community relevance and integrity. Methodologically, we employed focus groups for a comprehensive exploration of the advantages and disadvantages of AI in social services, and a World Cafe to gain expertise and perspective around four central themes: (1) data-driven social services for resource allocation, (2) predictive analytics and early intervention, (3) evaluation and continuous improvement, and (4) stakeholder collaboration. The discussion highlights the necessity of continuous ethical reassessment and updates within organizations to maintain integrity, especially in the context of AI in social services, emphasizing the importance of aligning with evolving societal norms and technological advancements. Additionally, it emphasizes the role of organizational culture and digital literacy in adopting AI, advocating for a balanced approach that integrates technological innovation with human empathy and judgement, while addressing the challenges of ensuring ethical AI deployment through proactive, inclusive, and culturally sensitive practices.

La salut pública ha de ser vista com un bé públic global, una inversió essencial no només per a la salut individual, sinó també per a la seguretat i la prosperitat a llarg termini de les societats. Conceptes com «One Health» i «Salut Planetària» reconeixen la interconnexió entre la salut humana, animal i ambiental, i subratllen la necessitat de col·laboració internacional per fer front a les amenaces globals, com el canvi climàtic, les pandèmies futures i la seguretat alimentària.

The paper focuses on the challenge of providing contextualized, value-sensitive, and participatory Artificial Intelligence (AI) that meets societal needs. The "AI for social Assessment" (AI FORA) project combines empirical research, gamification, and agent-based models (ABM) to assess the fairness of AI-based distribution in different countries and propose improvements. The paper presents a case study, where ABM and serious games are used to identify more desirable social assessment routines. A machine learning (ML) system is developed. The workflow for prototyping AI social assessment systems involves creating a synthetic population, generating synthetic outcomes based on improved assessment rules, training a neural network, and evaluating the effectiveness of the AI system. By following this approach, the paper suggests that unintended consequences and ineffective systems can be avoided, saving on costly development. The aim is to ensure that AI for social service delivery is responsive, fair, and beneficial to society.