Conference Agenda

The potential of generative artificial intelligence (GenAI) has been explored extensively by branches of economics as a transformative exogenous shock that will accelerate a positive economy-wide impact on innovation, productivity, and growth. However, its systemic socio-economic implications remain underexplored, often reduced to anecdotal policy debates about “AI’s perils to democracy” with robust data-driven evidence absent. This research addresses this gap by examining the economic and institutional determinants of GenAI ownership structures.

The primary aim of this study is to problematise ownership models and their influence on public goods provision and democratic accountability. Using comparative policy analyses, Synthetic Control, and Difference-in-Differences designs across the UK, the U.S, Australia, China, Singapore, and select EU member states, this study quantifies welfare and efficiency trade-offs under varying levels of regulatory oversight. Preliminary findings suggest that optimal GenAI-driven public goods provision such as healthcare and education is achieved through public or hybrid ownership.

Further, this study challenges conventional modernisation theories which assume a linear relationship between technological progress, economic growth, and democratic accountability. It suggests an inverse relationship between private GenAI ownership and public trust, and instead finds that countries with public or hybrid ownership models exhibit stronger democratic institutional strength.

This study makes significant contributions by introducing the concept of digital quasi-public goods and quantifying the impact of algorithmic ownership on democratic accountability. Most significantly and highly timely, this study will offer a blueprint to shape national strategies such as the EU AI Act and provide theoretical underpinnings for future research.

The rapid development of AI technologies and their critical economic and geopolitical significance have motivated countries to develop governance strategies to assert leadership in the AI era. These strategies aim to maximize the benefits of AI technologies while mitigating potential risks. Among the key players, the EU and China stand out as two major actors actively promoting their distinct AI governance approaches. We characterize China’s primary focus on fostering innovation, coupled with its more recent emphasis on improving regulations, and the EU’s prioritization of fundamental rights, with increasing attention to strategic autonomy and geopolitical considerations. Considering the fundamental differences between China’s and the EU’s AI strategies, as well as their recent evolutions, this paper adopts a role-theoretical perspective to assess how the changing roles of China and the EU—shaped by internal expectations and external contexts—explain their shifting focus in AI governance. More importantly, the paper explores the implications of these changing roles within the global AI governance framework, particularly in terms of their potential for cooperation and/or competition. The analysis is based on qualitative text analysis of policy documents and expert interviews.

Digitization and digital policy both in the EU and globally encompass many actors and various types of relationships, including individuals, consumers, businesses, governments, governmental agencies, authorities, etc. In this universe, academics and universities, although being an integral part of it are hardly mentioned. Nevertheless, in particular AI as a part of progressive digitization, now is almost an indispensable piece of discussion at any conference or academic meeting, not to mention number of papers that have been published so far. Many of them have been written with use of AI too.

It goes without saying that AI has triggered revolution in many aspects of academic life. There are number of questions on how to live in the new reality, in particular concerning use of GenAI by both academics and students. For the time being, however, those issues are not a subject of general discussion or the EU law, but rather internal regulations or guidelines adopted by some universities in Europe and beyond (see examples TUM Data Protection Guideline for the use of AI (https://www.digitalisierung.tum.de/en/tum-guideline-for-the-use-of-ai/); King’s guidance on generative AI for teaching, assessment and feedback (https://www.kcl.ac.uk/about/strategy/learning-and-teaching/ai-guidance); Initial guidelines for the use of Generative AI tools at Harvard (https://huit.harvard.edu/ai/guidelines).

Having said that, the main aim of this paper is to discuss the way AI changes academic world in its many dimensions, including but not limited to teaching, research, publishing papers and supervising of master and PhD/DBA thesis. To this end, one of the relevant questions is if a unified approach to use of GenAI in academic world is needed or not, at least across the EU. Adopting different models of permitted use of GenAI in academia, may raise question of equality in relation to teaching, evaluation of students’ works as well as academic degrees obtained under different models, in particular in case of public institutions (e.g. public universities and schools). Recognition of academic diplomas across the EU is another issue to be considered in this context. Therefore, the main goal of the paper is to discuss how to use GenAI in academia, but also if any intervention at the EU or the EU member states’ level is needed or required. Furthermore, the paper is also to see the way AI may develop in academia, and to the extent limited by our current knowledge, to give some recommendations in this respect.

This paper critically examines one of the key factors of the Brussels Effect: regulatory capacity, defined as a jurisdiction’s capability to produce stringent legislation, which is increasingly significant in global technology governance. Drawing on the recent European Artificial Intelligence Act and using socio-semantic network analysis, the paper argues that the involvement of a higher number of private sector actors with diverse lobbying power complicates the regulation beyond its initial intent. This complexity reveals that the Brussels Effect is not simply the smooth alignment of various EU powers creating high-quality regulation. Rather, it is an equilibrated regulatory phenomenon influenced by private sector both inside and outside the EU, each pursuing their own self-interests. This regulatory dynamic also challenges the portrayal of the EU as a regulator unafraid of the adverse effects of stringent regulation against the private sector demands. Instead, the growing lobbying power of the private sector is leading to a ‘Lobby-driven Brussels Effect,’ which adds a new dimension to its de facto and de jure forms.