Mark Fenwick

Paulius Jurcys, Mark Fenwick

This papers explores the question of human authorship when works are created with generative AI tools.

Mark Fenwick, Erik P. M. Vermeulen, Marcelo Corrales Compagnucci

Identifying and then implementing an effective response to disruptive new AI technologies is enormously challenging for any business looking to integrate AI into their operations, as well as regulators looking to leverage AI-related innovation as a mechanism for achieving regional economic growth. These business and regulatory challenges are particularly significant given the broad reach of AI, as well as the multiple uncertainties surrounding such technologies and their future development and effects. This article identifies two promising strategies for meeting the AI challenge, focusing on the example of Fintech. First, dynamic regulation, in the form of regulatory sandboxes and other regulatory approaches that aim to provide a space for responsible AI-related innovation. An empirical study provides preliminary evidence to suggest that jurisdictions that adopt a more proactive approach to Fintech regulation can attract greater investment. The second strategy relates to so-called innovation ecosystems. It is argued that such ecosystems are most effective when they afford opportunities for creative partnerships between well-established corporations and AI-focused startups and that this aspect of a successful innovation ecosystem is often overlooked in the existing discussion. The article suggests that these two strategies are interconnected, in that greater investment is an important element in both fostering and signaling a well-functioning innovation ecosystem and that a well-functioning ecosystem will, in turn, attract more funding. The resulting synergies between these strategies can, therefore, provide a jurisdiction with a competitive edge in becoming a regional hub for AI-related activity.

Marcelo Corrales Compagnucci, Mark Fenwick, Helena Haapio

This chapter explores the essential role of Binding Corporate Rules (BCRs) in managing and facilitating secure health data transfers within corporate groups under the EU General Data Protection Regulation (GDPR). BCRs are tailored to ensure compliance with the GDPR and similar international data protection laws, presenting a flexible mechanism for transferring sensitive health and genomic data. The chapter situates BCRs within the broader spectrum of the GDPR international data transfer mechanisms, addressing the unique challenges posed by the sensitive nature of health data and the increased adoption of AI technologies. The European Data Protection Board (EDPB) Recommendations 1/2022 on BCRs, issued following the Schrems II decision, are critically analyzed, highlighting their stringent requirements and the need for a balanced approach that prioritizes data protection and an AI governance framework. The chapter outlines the BCR approval process, stressing the importance of streamlining this process to encourage broader adoption. It underscores the necessity of a multidisciplinary approach in developing BCRs, incorporating recently adopted international standards and frameworks, which offer valuable guidance for organizations to build trustworthy AI management systems. They guarantee the ethical development, deployment, and operation of AI, which is essential for its successful integration and the broader digital transformation. In conclusion, BCRs are positioned as essential tools for secure health data management, fostering transparency, accountability, and collaboration across international borders. The chapter calls for proactive measures to incentivize BCR adoption, streamline approval processes, and promote more innovative approaches, ensuring BCRs remain a robust mechanism for global data protection and compliance.

Paulius Jurcys, Mark Fenwick

This paper examines how the rapid deployment of multi-agentic AI systems is reshaping the foundations of copyright law and creative markets. It argues that existing copyright frameworks are ill-equipped to govern AI agent-mediated interactions that occur at scale, speed, and with limited human oversight. The paper introduces the concept of agentic copyright, a model in which AI agents act on behalf of creators and users to negotiate access, attribution, and compensation for copyrighted works. While multi-agent ecosystems promise efficiency gains and reduced transaction costs, they also generate novel market failures, including miscoordination, conflict, and collusion among autonomous agents. To address these market failures, the paper develops a supervised multi-agent governance framework that integrates legal rules and principles, technical protocols, and institutional oversight. This framework emphasizes ex ante and ex post coordination mechanisms capable of correcting agentic market failures before they crystallize into systemic harm. By embedding normative constraints and monitoring functions into multi-agent architectures, supervised governance aims to align agent behavior with the underlying values of copyright law. The paper concludes that AI should be understood not only as a source of disruption, but also as a governance tool capable of restoring market-based ordering in creative industries. Properly designed, agentic copyright offers a path toward scalable, fair, and legally meaningful copyright markets in the age of AI.