This article examines the implementation of Anticipatory Artificial Intelligence (AI) Governance within the Provincial Council of Gipuzkoa (Basque Country) through an action research project designed to inform Territorial Digital Inclusion Strategies led by the General Directorate for Human Rights and Democratic Culture (Presidency). Situating the study within debates on anticipatory governance, it addresses the following research question: how can city-regional governments operationalise anticipatory AI governance to advance territorial digital inclusion while safeguarding democratic accountability and human digital rights? Moving beyond industrial and productivity-centred approaches, the study focuses on the ethical, socio-economic, and territorial implications of AI for citizens. The action research follows an eight-step methodological roadmap (2024–2026) driven by a Science-for-Policy approach. Methodologically, the study integrates a multi-actor action research design structured around (i) Knowledge Exchange, (ii) Stakeholder Engagement, and (iii) Open-Science Dissemination. Findings indicate that embedding anticipatory AI governance within territorial digital inclusion strategies enhances policy foresight, strengthens ethical reflexivity in algorithmic decision-making, and consolidates public trust through participatory governance. The analysis also incorporates an emerging legal dimension, informed by the leading author’s selection to recent European Commission initiatives, including the Frontier AI Expert Forum and the AI Act Advisory Forum, enabling preliminary alignment with evolving EU AI regulatory frameworks. The Gipuzkoa case offers a context-sensitive governance framework, highlighting design principles that may inform other territorial administrations under varying institutional and resource conditions. The central empirical contribution of the article is to demonstrate that anticipatory AI governance capacity is unevenly distributed across civil society organisations (CSOs), provincial directorates, and municipalities. These asymmetries affect how AI-related risks, digital inclusion priorities, and governance responsibilities are interpreted and operationalised across territorial governance scales. Consequently, anticipatory AI governance emerges not as a universally replicable institutional model, but as a context-sensitive governance capability conditioned by administrative capacity, territorial coordination, and stakeholder participation.

Contemporary AI governance typically treats ethics and law as complementary normative domains with different levels of enforcement. This paper argues that this distinction alone is insufficient given the evolving role of AI systems as artificial moral agents. Conceptualizing ethics and law as functionally distinct yet interdependent domains, the authors argue that the notion of functional agency to describe how AI systems generate normatively relevant outcomes through decision substitution, the embedding of societal norms in the design of AI systems, and behavioural steering. Drawing on case studies of recommender systems, large language models, autonomous driving, and care robotics, the paper demonstrates the systematic displacement of micro-level ethical reasoning by standardized, societal logics that tend to prioritize macro-level considerations such as beneficence over individual autonomy. This shift poses a structural challenge to human rights, which explicitly protect individual moral deliberation. The paper therefore calls for a paradigm shift in AI governance from embedding societal norms in AI systems to governing the construction of normativity itself grounded with an emphasis on the protection of individual ethical reasoning and stronger acknowledgement of moral pluralism.

Acceleration ethics addresses the tension between innovation and safety in artificial intelligence. The acceleration argument is that risks raised by innovation should be answered with still more innovating. This paper summarizes the theoretical position, and then shows how acceleration ethics works in a real case. To begin, the paper summarizes acceleration ethics as composed of five elements: innovation solves innovation problems, innovation is intrinsically valuable, the unknown is encouraging, governance is decentralized, and ethics is embedded. Subsequently, the paper illustrates the acceleration framework with a use-case, a generative artificial intelligence language tool developed by the Canadian telecommunications company Telus. While the purity of theoretical positions is blurred by real-world circumstances, the Telus experience documents acceleration AI ethics as a way of maximizing social responsibility through innovation.