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Large Language Models (LLM) have experienced strong development in recent years, with varied applications. This paper uses LLMs to develop a post-hoc process that provides more elaborated explanations of the results of food recommendation systems. By combining LLM with a hybrid extraction of key variables using SHAP, we obtain dynamic, convincing and more comprehensive explanations to lay user, compared to those in the literature. This approach enhances user trust and transparency by making complex recommendation outcomes easier to understand for a lay user.

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.

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.

The Digital Omnibus Proposal aims to streamline the European Union’s digital regulatory framework but raises important concerns. This paper highlights risks related to reduced traceability of AI training data, weakened links between data governance and high-risk classification, and potential inconsistencies arising from simplified data access and reporting mechanisms. It argues that these changes may undermine effective risk assessment, shift complexity to downstream actors, and create legal uncertainty. To address these issues, the paper proposes targeted recommendations, including enhanced transparency and notification requirements for AI training data, safeguards to ensure that data availability does not affect risk classification, ex ante assessments for high-risk data reuse, and stronger governance and accountability measures for the centralised incident reporting system. These measures aim to preserve regulatory coherence, risk sensitivity, and the EU’s broader objectives of trustworthy and sovereign AI governance.

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.

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.