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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.

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.

The “right to contest” decisions that have consequences on individuals or the society is a well-established democratic right. Contesting a decision is not a matter of simply providing an explanation, but rather of assessing whether the decision and the explanation are permissible against an organization's governance framework. Yet, albeit the popularity of adjacent fields, little work has been explicitly done on contesting AI decisions. In this paper, we propose that formal argumentation can be used to formulate contestations of decisions made by artificial agents. We extend the discourse on socio-ethical values in AI by conceptualizing our argumentation framework as a formal dialogue, enabling the interaction between humans and agents as decisions are being contested.

Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices through online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often “unintended” systemic outcomes. This paper introduces human-AI coevolution as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: (i) outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; (ii) propose a reflection at the intersection between complexity science, AI and society; (iii) provide real-world examples for different human-AI ecosystems; and (iv) illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i.e., scientific, legal and socio-political.

A sociotechnical systems lens on AI is often used to bring attention to the human factors and societal impacts that are often neglected through technical abstraction. However, abstraction is also a general principle of sociotechnical systems, where functional objectives (e.g. fair hiring decisions) are operationalised into low-level implementations (e.g. fair algorithms, recourse, legal basis). The trouble with abstraction arises when critical contextual factors are erroneously neglected, leading to an impoverished representation of the problem space. De-contextualisation can render the resulting solutions problematic when they are re-contextualised back into the site of use, where misabstractions may produce safety hazards, harms, moral wrongs, and context frictions. Despite growing recognition that context matters for how sociotechnical systems operate in practice, the normative implications of abstraction are still understudied. In this paper, we propose misabstraction as an analytic framework for thinking about the perils and challenges of sociotechnical abstraction. We use the framework to analyse the requirements specification outlined in the procurement tender of a recommender system for public employment services and show how misabstractions cascade through the sociotechnical stack, producing ripple effects that implicate hidden and neglected contextual factors across multiple frames (e.g. institutional, organisational, operational, and algorithmic). Misabstraction can help policymakers, system designers, critical scholars, and civil society alike to attend to the political conditions that shape design, and their implications for understanding and addressing systemic risk in sociotechnical AI systems.

As AI systems become increasingly autonomous and embedded in socio-technical environments, balancing innovation with social responsibility grows increasingly urgent. Multi-agent systems and autonomous agents offer valuable insights into decision-making, coordination, and adaptability, yet their deployment raises critical ethical and governance challenges. How can we ensure that AI aligns with human values, operates transparently, and remains accountable within complex social and economic ecosystems? This talk explores the intersection of AI ethics, governance, and agent-based perspectives, drawing on my work in AI policy and governance, as well as prior research on agents, agent organizations, formal models, and decision-making frameworks [6,7]. Recent advancements are reshaping AI not just as a technology but as a socio-technical process that functions in dynamic, multistakeholder environments. As such, addressing accountability, normative reasoning, and value alignment requires a multidisciplinary approach [2]. A central focus of this talk is the role of governance structures, regulatory mechanisms, and institutional oversight in ensuring AI remains both trustworthy and adaptable. Drawing on recent AI policy research [1], I will examine strategies for embedding ethical constraints in AI design, the role of explainability in agent decision-making, and how multi-agent coordination informs regulatory compliance. Rather than viewing regulation as a barrier, will show that responsible governance is an enabler of sustainable innovation, driving public trust, business differentiation, and long-term technological progress [4]. By integrating insights from agent-based modeling, AI policy frameworks, and governance strategies, this talk underscores the importance of designing AI systems that are both socially responsible and technically robust [5,3]. Ultimately, ensuring AI serves the common good requires a multidisciplinary approach-one that combines formal models, ethical considerations, and adaptive policy mechanisms to create AI systems that are accountable, fair, and aligned with human values.

With Artificial intelligence (AI) development and adoption advancing at an unprecedented pace, policymakers and regulators are encountering both significant challenges and opportunities. The challenges emerge from the disconnect between the fragmented & at times siloed policy & technology landscapes, whilst the opportunities relate to novel insights and capabilities afforded to decision-makers to strengthen the evidence base for sustainable policy. While AI offers transformative potential, it also poses substantial risks, such as biases, inequalities, and threats to privacy and security. In this context, AI policy research has emerged as an essential guide to navigating the complex interplay between technological innovation and societal impact. It ensures that advancements in AI align with ethical, legal, and social priorities. AI policy research provides the evidence base needed to address these challenges, fostering accountability, transparency, fairness, and inclusivity in AI governance. It also helps anticipate future regulatory needs, bridge the gap between stakeholders, and ensure that AI technologies are deployed responsibly and equitably, contributing to sustainable development and the public good.

This roadmap, developed through collaborative discussions at the recent AI Policy Research Summit, reflects a shared vision for advancing research on responsible AI policy and governance. It emphasizes the critical role of policy research inensuring that AI development is guided by robust evidence, ethical considerations, and a commitment to sustainability and inclusivity. By prioritizing transparency, accountability, and the well-being of humans and the planet, this roadmap highlights how research can inform global approaches to AI governance, addressing complex societal needs and ethical challenges. It serves as a guiding framework for stakeholders across academia, industry, government, and civil society to collaborate in generating actionable insights and evidence-based strategies, noting that while evidence-based AI policy often draws on data-driven research, it equally values critical theoretical insights and fundamental rights approaches, ensuring a holistic understanding that extends beyond the purely quantifiable.

The United Nations (UN) put out a questionnaire and request for public comment on a proposal to form an Independent International Scientific Panel on Artificial Intelligence (AI), as well as a Global Dialogue on AI in the context of the Global Digital Compact. The questionnaire was sent out in late 2024, and the following brief report forms the response submitted by the AI Policy Lab at Umeå University, Sweden.