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The Routledge Handbook of European Integrations fills a significant gap in the European studies literature by providing crucial and groundbreaking coverage of several key areas that are usually neglected or excluded in European integration collections. Whilst still examining the largest and most influential institutions, bodies and highly funded policy areas as acknowledged dominant topics in European studies, it crucially does so with much greater balance by devoting equal billing to areas such as such as culture in European integration or new technologies and their impact on the EU. Organised around three main sections – culture, technology and 'tangibles' – the book:
• offers an authoritative 'encyclopaedia' to 'alternative' areas in European integration, from media, football, Erasmus and Tourism, to transport, space, AI and energy;
• retains coverage of the dominant topics in European Studies, such as the Eurozone, the Common Internal Market, or European Law, but in balance with other areas of interest;
• provides an essential companion to existing scholarship in European Studies.
The Routledge Handbook of European Integrations is essential reading and an authoritative reference for scholars, students, researchers and practitioners involved in, and actively concerned about, research in the study of European integration/studies.

This chapter focuses on the governance of dual-use research in neuroscience in the EU. At a time when many countries are making unprecedented investments in the field of neuroscience, which sometimes are described as the 'gold rush' or 'golden age' of neuroscience, the European Commission has been supporting neuroscience – or brain research as it is sometimes called – via its research and innovation FP. In the FP7 that lasted from 2007–2013, the Commission invested €3.1 billion in neuroscience. In the first five years (until November 2018) of the following Horizon 2020 programme, a similar sum of €3.2 billion was invested (European Commission 2019a). This funding supports a number of neuroscience research projects included within the Future and Emerging Technologies (FET) programme.

Abstract
The launch of ChatGPT in late 2022 led to major controversies about the governance of generative artificial intelligence (AI). This article examines the first international governance and policy initiatives dedicated specifically to generative AI: the G7 Hiroshima process, the Organisation for Economic Cooperation and Development reports, and the UK AI Safety Summit. This analysis is informed by policy framing and governance literature, in particular by the work on technology governance and Responsible Innovation. Emerging governance of generative AI exhibits characteristics of polycentric governance, where multiple and overlapping centers of decision-making are in collaborative relationships. However, it is dominated by a limited number of developed countries. The governance of generative AI is mostly framed in terms of the risk management, largely neglecting issues of purpose and direction of innovation, and assigning rather limited roles to the public. We can see a "paradox of generative AI governance" emerging, namely, that while this technology is being widely used by the public, its governance is rather narrow. This article coins the term "governance fix" to capture this rather narrow and technocratic approach to governing generative AI. As an alternative, it suggests embracing the politics of polycentric governance and Responsible Innovation that highlight democratic and participatory co-shaping of technology for social benefit. In the context of the highly unequal distribution of power in generative AI characterized by a high concentration of power in a small number of large tech companies, the government has a special role in reshaping the power imbalances by enabling wide-ranging public participation in the governance of generative AI.

While artificial intelligence (AI) is often presented as a neutral tool, growing evidence suggests that it exacerbates gender, racial, and other biases leading to discrimination and marginalization. This study analyzes the emerging agenda on intersectionality in AI. It examines four high‐profile reports dedicated to this topic to interrogate how they frame problems and outline recommendations to address inequalities. These four reports play an important role in putting problematic intersectionality issues on the political agenda of AI, which is typically dominated by questions about AI's potential social and economic benefits. The documents highlight the systemic nature of problems that operate like a negative feedback loop or vicious cycle with the diversity crisis in the AI workforce leading to the development of biased AI tools when a largely homogenous group of white male developers and tech founders build their own biases into AI systems. Typical examples include gender and racial biases embedded into voice assistants, humanoid robots, and hiring tools. The reports frame the diversity situation in AI as alarming, highlight that previous diversity initiatives have not worked, emphasize urgency, and call for a holistic approach that focuses not just on numbers but rather on culture, power, and opportunities to exert influence. While dedicated reports on intersectionality in AI provide a lot of depth, detail, and nuance on the topic, in the patriarchal system they are in danger of being pigeonholed as issues of relevance mainly for women and minorities rather than part of the core agenda.

AbstractThis study examines the policy discussions surrounding the purpose of the development and use of an emerging technology. It applies the two stylized technology policy frames of economic growth and societal challenges to analyse framing of one of the key emerging technologies today—Artificial Intelligence (AI). It demonstrates that recent AI policy documents include both—economic growth as well as societal challenges—frames. While AI is a novel technology, its recent policy builds on traditional ideas about the role of technology in facilitating economic growth and competitiveness supported by well-known measures such as investment in research and highly skilled workforce. Additionally, AI policy draws on more recent frame on the contribution of technology to addressing societal challenges and the Sustainable Development Goals but presents AI as a technological solution to complex societal issues. While some interest in addressing both economic and social objectives in AI policy can be observed, the policy documents remain silent about their compatibility.

The aim of this article is to analyse the evolution of the ideational framework of the most ambitious initiative in supranational research governance so far – the European Research Area (ERA), launched by the European Commission in 2000. In order to do so, the ERA initiative is analysed against the background of the long-term development of the science, technology and innovation policy ideas. The analysis reveals that over the course of 14 years, the policy aims of, and support for, the ERA initiative have considerably broadened. While economic competitiveness goals initially dominated the Commission's initiative to launch the ERA, the initiative has gradually expanded towards social and scientific aims as well as stronger involvement of member states and stakeholders. Recent "big ideas" of excellent science and Grand Challenges help increase support for the ERA initiative among the research community and society. In the broadened ERA ideational frame, diverse aims of scientific freedom, societal relevance and economic competitiveness co-exist but attention to the relationships between them has been limited. Further exploration and conceptualization of the relationships between diverse policy ideas is an important challenge for future research policy studies and practice.

AbstractWhile recent discussions about Artificial Intelligence (AI) as one of the most powerful technologies of our times tend to portray it as a predominantly technical issue, it also has major social, political and cultural implications. So far these have been mostly studied from ethical, legal and economic perspectives, while politics and policy have received less attention. To address this gap, this special issue brings together nine research articles to advance the studies of politics and policy of AI by identifying emerging themes and setting out future research agenda. Diverse but complementary contributions in this special issue speak to five overarching themes: understanding the AI as co‐shaped by technology and politics; highlighting the role of ideas in AI politics and policy; examining the distribution of power; interrogating the relationship between novel technology and continuity in politics and policy; and exploring interactions among developments at local, national, regional and global levels. This special issue demonstrates that AI policy is not an apolitical field that can be dealt with just by relying on knowledge and expertise but requires an open debate among alternative views, ideas, values and interests.

AbstractBias is a key issue in expert and public discussions about Artificial Intelligence (AI). While some hope that AI will help to eliminate human bias, others are concerned that AI will exacerbate it. To highlight political and power aspects of bias in AI, this contribution examines so far largely overlooked topic of framing of bias in AI policy. Among diverse approaches of diagnosing problems and suggesting prescriptions, we can distinguish two stylized framings of bias in AI policy—one more technical, another more social. Powerful technical framing suggests that AI can be a solution to human bias and can help to detect and eliminate it. It is challenged by an alternative social framing, which emphasizes the importance of social contexts, balance of power and structural inequalities. Technological frame sees simple technological fix as a way to deal with bias in AI. For the social frame, we suggest to approach bias in AI as a complex wicked problem, for which a broader strategy is needed involving diverse stakeholders and actions. The social framing of bias in AI considerably expands the legitimate understanding of bias and the scope of potential actions beyond technological fix. We argue that, in the context of AI policy, intersectional bias should not be perceived as a niche issue but rather be seen as a key to radically reimagine AI governance, power and politics in more participatory and inclusive ways.

This editorial introduces the notion of the Europe of Knowledge and places it in the European integration research agenda. We first describe what the concept means before suggesting how to approach the Europe of Knowledge as a new case for investigating European integration dynamics. This discussion revolves around the evolution of policy developments in research and higher education to show how knowledge policies are compound and manifest distinct dimensions of differentiated integration and experimentation, both fruitful theoretical research agendas. We then summarise the articles to show the respective Europe of Knowledge themes they highlight. We conclude by considering how the Europe of Knowledge in the making encourages testing established empirical and analytical assumptions about European integration and experimenting with emerging ideas about regional cooperation from around the world.

ABSTRACT
This paper examines how the governance in AI policy documents have been framed as way to resolve public controversies surrounding AI. It draws on the studies of governance of emerging technologies, the concept of policy framing, and analysis of 49 recent policy documents dedicated to AI which have been prepared in the context of technological hype expecting fast advances of AI that will fundamentally change economy and society. The hype about AI is accompanied by major public controversy about positive and negative effects of AI. Against the backdrop of this policy controversy, governance emerges as one of the frames that diagnoses the problems and offers prescriptions. Accordingly, the current governance characterized by oligopoly of a small number of large companies is indicated as one of the reasons for problems such as lack of consideration of societal needs and concerns. To address these problems, governance frame in AI policy documents assigns more active and collaborative roles to the state and society. Amid public controversies, the state is assigned the roles of promoting and facilitating AI development while at the same time being a guarantor of risk mitigation and enabler of societal engagement. High expectations are assigned to public engagement with multiple publics as a way to increase diversity, representation and equality in AI development and use. While this governance frame might have a normative appeal, it is not specific about addressing some well-known challenges of the proposed governance mode such as risks of capture by vested interests or difficulties to achieve consensus.