The Artificial Intelligence Act (AI Act) of the European Union (EU) claims to be based on a risk-based approach to avoid over-regulation and to respect the principle of legislative proportionality. This paper argues that risk-based regulation is indeed the right approach to AI regulation. At the same time, however, the paper shows that important provisions of the AI Act do not follow a truly risk-based approach. Yet, this is nothing that cannot be fixed. The AI Act provides for sufficient tools to support future-proof legislation and to implement it in line with a genuine risk-based approach. Against this background, the paper analyses how the AI Act should be applied and implemented according to its original intention of a risk-based approach, and what lessons legislators around the world can learn from the AI Act in regulating AI.

The analysis of liability aspects facing Artificial Intelligence (‘AI’)-generated outputs under copyright and related rights has been overlooked compared to other issues connected to the development and use of AI. This study fills this gap by exploring pertinent questions under international, EU and UK law. Specifically, the study tackles actionable reproduction, allocation of liability, and availability of defences. The analysis ultimately shows that, while it is clear that each case will need to be decided on its own merits, the generative AI output phase raises several profiles of liability under copyright law. If the goal of policymakers and relevant stakeholders is to ensure the balanced and sustainable development of AI, then the issues related to the generation and dissemination of AI outputs need to be given ample attention and a greater role in the debate than what has been the case so far, whether it is in the context of risk assessment and compliance, licensing initiatives, or in contentious scenarios.

Regulation is nothing without enforcement. This particularly holds for the dynamic field of emerging technologies. Hence, this article has two ambitions. First, it explains how the EU’s new Artificial Intelligence Act (AIA) may be implemented and enforced by various institutional bodies, thus clarifying the governance framework of the AIA. Second, it proposes a normative governance model, providing recommendations to ensure uniform and coordinated execution of the AIA and the fulfillment of the legislation. The article explores how the AIA may be implemented by national and EU institutional bodies, encompassing longstanding bodies, such as the European Commission, and those newly established under the AIA, such as the AI Office. It investigates their roles across supranational and national levels, emphasising how EU regulations influence institutional structures and operations. These regulations may not only directly dictate the structural design of institutions but also indirectly request administrative capacities needed to enforce the AIA.

This Article explores the implications of Artificial Intelligence (AI)-driven innovation across sectors, highlighting the resulting legal uncertainties. Despite the transformative influence of AI in healthcare, retail, finance and more, regulatory responses to these developments are often contradictory, contributing to the opacity of the legal underpinnings of AI business. Our Article notes the common trend of commercializing AI tools amidst legal uncertainty, using innovative contractual solutions to secure claims. Over time, these innovations trigger overlooked legal conflicts, sometimes leading to outright bans on AI products due to negative impacts on some fundamental rights and democratic Governance. The core argument of our Article is that an over-reliance on co-regulatory strategies, such as those proposed by the European AI Act, exacerbates legal instability in emerging technological markets. This panorama creates an ’extended legal present’ when alternative legal expectations coexist, thus causing economic and political uncertainty that may elicit legal instability in the future. The concept of ’competing legal futures’ is introduced to illustrate how economic actors must bet on a legal future in the absence of guarantees that this future will materialize. To help analyze this complex narrative, we propose a theoretical framework for understanding legal, technological, and economic dynamics, highlighting anomalies in market exchanges within the co-regulatory model. Despite the focus on European developments, the practical and theoretical implications extend beyond the EU, making the Article relevant to a broader understanding of the legal-economic challenges posed by AI and digital innovation. We conclude by arguing for a course correction, proposing institutional diversification for resilient governance of legal innovation under uncertainty.

This Article addresses the pressing issues surrounding the use of automated systems in public decision-making, specifically focusing on migration, asylum, and mobility. Drawing on empirical data, this Article examines the potential and limitations of the General Data Protection Regulation and the Artificial Intelligence Act in effectively addressing the challenges posed by automated decision-making (ADM). The Article argues that the current legal definitions and categorizations of ADM fail to capture the complexity and diversity of real-life applications where automated systems assist human decision-makers rather than replace them entirely. To bridge the gap between ADM in law and practice, this Article proposes to move beyond the concept of “automated decisions” and complement the legal protection in the GDPR and AI Act with a taxonomy that can inform a fundamental rights analysis. This taxonomy enhances our understanding of ADM and allows to identify the fundamental rights at stake and the sector-specific legislation applicable to ADM. The Article calls for empirical observations and input from experts in other areas of public law to enrich and refine the proposed taxonomy, thus ensuring clearer conceptual frameworks to safeguard individuals in our increasingly algorithmic society.

Nowadays public policymakers are offered with opportunities to take data-driven evidence-based decisions by analyzing the very large volumes of policy-related data that are generated through different channels (e.g., e-services, mobile apps, social media). Machine learning (ML) and artificial intelligence (AI) tehcnologies ease and automate the analysis of large policy-related datasets, which helps policymakers to realize a shift toward data-driven decisions. Nevertheless, the deployment and use of AI tools for public policy development is also associated with significant technical, political, and operation challenges. For instance, AI-based policy development solutions must be transparent and explainable to policymakers, while at the same time adhering to the mandates of emerging regulations such as the AI Act of the European Union. This paper introduces some of the main technical, operational, regulatory compliance challenges of AI-based policymaking. Accordingly, it introduces technological solutions for overcoming them, including: (i) a reference architecture for AI-based policy development, (ii) a virtualized cloud-based tool for the specification and implementation of ML-based data-driven policies, (iii) a ML framework that enables the development of transparent and explainable ML models for policymaking, and (iv) a set of guidelines for using the introduced technical solutions to achieve regulatory compliance. The paper ends up illustrating the validation and use of the introduced solutions in real-life public policymaking cases for various local governments.

The proposed Artificial Intelligence Act (AI Act) is the first comprehensive attempt to regulate artificial intelligence (AI) in a major jurisdiction. This article analyses Article 9, the key risk management provision in the AI Act. It gives an overview of the regulatory concept behind the norm, determines its purpose and scope of application, offers a comprehensive interpretation of the specific risk management requirements and outlines ways in which the requirements can be enforced. This article can help providers of high-risk systems to comply with the requirements set out in Article 9. In addition, it can inform revisions of the current draft of the AI Act and efforts to develop harmonised standards on AI risk management.

Transparency is widely acknowledged as a core value in the governance of artificial intelligence (AI) technologies. However, scholarship on AI technologies and their regulation often casts this need for transparency in terms of requirements for the explanation of algorithmic outputs and/or decisions produced with the involvement of opaque black-box AI systems. Our article argues that this discourse has re-interpreted and reshaped transparency in fundamental ways away from its original meaning. The target of transparency – in most cases, the provider of AI software – determines and shapes what is made visible to the outside world, and there is no external check on the validity and accuracy of such mediated accounts and explanations, opening transparency up for manipulation. Through a theoretically informed and critical analysis of the transparency provisions in the European Union’s AI Act proposal, the article shows that the substitution of transparency with mediated explanations faces important technical constraints, creates opportunities and incentives for both providers and public-sector users of AI systems to adopt opaque practices, and reinforces secrecy requirements that gag accountability in practice. An approach to transparency as disclosure thus becomes necessary, even if not sufficient in and of itself, to ensure the accountable development and use of AI technologies in the European Union. Transparency needs to be reclaimed as a core concept, accountability tailored and reinforced and the necessity for secrecy re-examined and cordoned off.