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There is broad consensus that disciplinary higher education must include artificial intelligence (AI) content tailored to the needs of different domains. Such an approach to higher education will help cultivate the skills, knowledge and critical thinking necessary for the socially beneficial development and integration of AI into the routines, organisations and infrastructures that shape society. Such an approach to higher education further aligns with broader policy and legal recommendations and requirements for upskilling, reskilling, AI literacy and interdisciplinary education to enable individuals to live with, work with, contribute to developing, and navigate AI technologies.

The paper provides insights on how an empirical approach to the pressing question of educating AI broadly in higher education can be rewardingly pursued. We focus on the findings from an interdisciplinary small-scale qualitative study. The study is based on a workshop conducted in 2024 in Uppsala University within the WASP-ED programme – a nation-wide educational and interdisciplinary programme aimed at increasing the capability and capacity of Swedish universities in providing relevant and scalable education in AI.

The aim of the workshop was to lay the ground for the development of course syllabi on AI -focused curricula across disciplines and academic institutions in Swedish higher education. 13 researchers across nine academic institutions participated in the workshop. The researchers, primarily from the social sciences and humanities, are all engaged in interdisciplinary scholarship related to AI.

The paper presents the theoretical grounding of the workshop and its methodological design, and outlines the analytical steps taken to identify major pedagogical principles and topics in AI that could be developed and integrated into educational curricula. The results outline five themes that emerged during the workshop: AI, diversity and human rights; Educating on the foundations of AI; Pushing the boundaries of theories and methodology; Building better AI systems; and Educating professionals within certain disciplines – changing professions.

We compiled the themes into a blueprint of an advanced level 60-credit programme, which could be developed into a 120-credit master’s programme. The programme consists of four modules: Foundations of AI in Society; AI in the Wild; Pushing Boundaries Building better AI; and Thesis. The participants agreed on three pedagogical principles to align the programme content and courses with societal needs: Learning by doing; Real world challenges; Engaging stakeholders. We evaluated the blueprint by mapping it to the WASP-ED AI Curriculum and identified different opportunities to extend the blueprint into a master’s programme.

We suggest that the blueprint can be used by educational institutions to create courses and programmes aimed at developing disciplinary education as well as fostering multidisciplinary understanding of AI in society. Furthemore, we suggest that the methods through which the blueprint was developed offer valuable insights on how interdisciplinary education on AI can be productively developed.

This submission to the United Nations on Global Dialogue on AI Governance argues for a shift from technology-led AI governance towards purpose-led governance that prioritises societal value, human rights and long-term sustainability. It emphasises that decisions about AI should begin with assessing whether AI is appropriate in a given context before considering how it should be implemented. The submission calls for governance approaches that are human-centred, evidence-based and grounded in democratic principles.

Key priorities include trustworthy AI, capacity building, human rights protection, transparency, accountability and interoperability across governance frameworks. The authors highlight concerns about growing inequalities in access to computing resources, data and expertise. They also draw attention to governance gaps in transboundary domains and the need to address the social, cultural and institutional impacts of AI adoption.

The submission advocates for stronger international cooperation through inclusive and participatory governance processes. It stresses the importance of meaningful involvement from underrepresented communities, Indigenous peoples and actors from the Global Majority. Practical tools, policy literacy initiatives and structured self-assessment tools are presented as ways to support responsible AI adoption. The submission concludes that effective AI governance requires coordinated action, capacity building and continuous reflection on the purposes, risks and consequences of AI systems.

Policymakers face unprecedented challenges in navigating the global politics of  artificial intelligence (AI) and healthcare. While AI offers transformative potential, it can exacerbatehealth inequities and contribute to negative health outcomes along its opaque, transnational value chain. This paper provides an overview of the most pressing global political concerns related to AI and healthcare that warrant policymakers’ attention. These are 1. Defining artificial intelligence, 2. The scales of global political discourse on AI and healthcare, 3. AI and the global political economy of healthcare, 4. The emerging global governance landscape, 5. Security and conflict, 6. Global political risks and limitations of AI (mis)use, 7. The global politics of health data in the age of AI, and 8. The environmental impacts of AI. By doing so the paper offers a currently under-represented global political perspective on the responsible adoption of AI in healthcare, to support policy makers the responsible adoption of AI in healthcare.

As many NATO states compete in the geopolitical “AI race,” their ambitions increasingly depend on energy- and data-intensive infrastructures. Central to this are subsea cables (SSCs), which have been denoted the “arteries of AI”. SSCs facilitate the national and transnational transfer of data and electricity necessary for current and future AI development. Despite their criticality, SSCs are highly vulnerable to sabotage and hybrid warfare, carrying profound social, political, economic, and security implications. This paper presents findings from an interdisciplinary expert workshop held in Stockholm in January 2026. The workshop focused on the protection of SSCs within NATO states, specifically the UK and the Nordic-Baltic region, amidst the ongoing geopolitical disorder and decline in multilateralism. We identify a “policy gap” and an “action gap” in protecting SSCs. Regarding the policy gap, the paper sets out three governance mechanisms that could be leveraged to better protect SSCs, including the reinterpretation of piracy under the United Nations Convention on the Law of the Sea (UNCLOS), the expansion of Exclusive Economic Zone (EEZ) enforcement, and the adoption of an armed conflict paradigm. Further, we discuss the action gap - while the legal mechanisms to protect these cables exist, many remain hesitant to act upon these. This action gap is explored through a preliminary typology of challenges (legal, geopolitical, global political economy, and technical) that hinder effective policy integration to secure these arteries of AI.

Dominant approaches to explainability in AI emphasise post hoc technical transparency, overlooking the socio-technical contexts in which systems are developed, deployed, and experienced. This paper argues that beginning AI adoption processes with Question Zero (Q0), “Should we adopt an AI system in the first place?”, reframes explainability as an essential requirement across the entire AI lifecycle rather than a narrow compliance task. Q0 challenges entrenched techno-solutionist assumptions that position AI as the default or best option, encouraging early integration of considerations of explainability in system design. By foregrounding this, Q0 shifts explainability towards purpose aligned, stakeholder aware forms that move beyond generic model centred outputs. Further, the paper reflects on how explainability can also strengthen Q0 by providing tools to assess the proportionality of AI adoption, clarify problem framing, and make visible the alternatives excluded during design. Taken together, these dual perspectives, Q0 for explainability and explainability for Q0, offer multidimensional opportunities for enhancing explainability. The paper illustrates this argument through reflection on the need, and value of, applying QO in the context of the European Commission’s Apply AI Strategy.

This perspective argues that the legitimacy of the UN Independent International Scientific Panel on Artificial Intelligence (IISPAI) depends as much on the demonstrable independence of its governance as on the quality of its scientific expertise. While the Preliminary Report offers an important contribution to global AI governance, it provides limited transparency regarding the management of funding, conflicts of interest, agenda-setting, and scientific disagreement. We contend that greater methodological and institutional transparency, alongside stronger structural safeguards for independence, is essential if the Panel is to serve as a trusted and authoritative source of scientific advice.

Organ donation is a crucial aspect of healthcare, yet, the number of donors is insufficient to cover the demand for transplant procedures. In the European Union, around 15 people die each day waiting for a life-saving organ. National policies differ greatly among countries, but it is unclear how successful policies affect Deceased Organ Donation rates when introduced in new settings. This paper explores the use of Agent-Based Social Simulation (ABSS) to inform organ donation policymaking. Simulations provide policy actors with a safe environment to investigate the consequences of different policy interventions without the risk of harming people. We present an agent-based model of the Swedish organ donation system, where we can investigate the impact of Spain’s policy approach, which produced the highest DOD rates in Europe. The results highlight the potential of ABSS as a tool for designing policy interventions in complex healthcare systems. Further developments can enable policymakers to identify successful strategies and monitor their effect to evaluate policy progression. 

The urgency of addressing the harms of AI chatbots was underscored in the recent US Senate Judiciary Committee hearing, “Examining the Harm of AI Chatbots.” During the hearing, parents of children recounted the harrowing stories of AI chatbot-influenced mental health emergencies, including self-harm and death, harms that were never inevitable. Instead, they were predictable consequences of a lax regulatory environment and a widespread culture of irresponsibility in the tech sector broadly, and Silicon Valley specifically. The 10th of September marks World Suicide Day, a stark reminder of why action is urgently needed on AI chatbots, as their intentional or unintentional misuse in mental health spaces proliferates.

This paper examines the European Commission’s “AI First” strategy, arguing that it places acceleration and economic competitiveness above democratic values, societal benefit, and human-centric innovation. While substantial investment in AI is welcome when it promotes sustainable, equitable, and responsible innovation, the authors warn that policy is shifting from governance to unchecked deployment, risking fragmentation, dependency, and misaligned priorities. Rather than asking how AI can be applied, the paper urges policymakers to ask why, advocating a “People First” approach grounded in societal needs, digital sovereignty, and responsible innovation. The authors argue that Europe’s AI leadership should be shaped not by speed, but by principled direction, inclusivity, and a commitment to long-term public value.

Contemporary AI policy is dominated by hegemonic ne-oliberal ideology, embedding assumptions of individualism,rationality, and market fundamentalism into its regulatoryframeworks. This is evident in major policy efforts (e.g., theEU AI Act or the OECD principles) which prioritize eco-nomic growth and innovation over justice, equity, and col-lective welfare, and in the current policy landscape that fa-vors market incentives and private sector leadership whilesidelining democratic control and structural critique. This pa-per questions these prevailing paradigms and exposes howthey reflect and reinforce capitalist power structures throughcorporate lobbying, the pursuit of specific kinds of AI mod-els motivated primarily by usefulness to capital, and the ex-ternalization of social and environmental costs. We argue thateffective AI governance must confront, rather than accommo-date, capitalist interests. Drawing on legal and political the-ory, we propose an explicitly anti-capitalist approach to AIpolicy, that centers on social well-being, redistributive justice,and democratic control over technological infrastructures. Indoing so, we outline essential counter-balancing policy ap-proaches to reclaim AI governance from capitalistic captureand advance just and sustainable technology futures.