Hannah Ruschemeier

The European Union has advanced its agenda towards regulatory simplification. At first glance, the Digital Omnibus can indeed be considered a recalibration of EU digital policy from a rights-based paradigm towards an approach increasingly shaped by competitiveness and efficiency. However, while the omnibus proposals raise legitimate concerns about the possible dilution of the Union’s constitutionally oriented approach, they do not merely represent a simplification effort and a retreat from regulation. This paper argues that the approach towards simplification has not reduced the path towards the regulatory expansion of European digital policy. While focusing towards simplification, the Union approach seems to increase risks for fundamental rights and legal certainty while continuing to expand its digital agenda, as in the case of the Digital Services Act. This dual movement, presented as a coupling of simplification and regulatory expansion, is likely to make European digital regulation even more convoluted, thus increasing risks for fundamental rights and legal certainty. By looking at the goals of European digital constitutionalism, which focuses on the reframing of rights and power in the algorithmic society, the paper contends that the EU’s priority should shift away from this dual track and should not rely on simplification as a way to address constitutional questions which would require a broader strategy as in the case of enforcement.

Dieser Beitrag beschreibt das Phänomen des Automation Bias aus psychologischer Perspektive. Automation Bias wird in der Verordnung zur Festlegung harmonisierter Vorschriften für Künstliche Intelligenz (KI-Verordnung, KI-VO) in Artikel 14 Abs. b) und in vielen Debatten zum Einsatz von Künstlicher Intelligenz (KI) in Hochrisikobereichen vor allem als Phänomen des übermäßigen Vertrauens dargestellt. Diese Darstellung wird jedoch der Komplexität des Phänomens kaum gerecht. Basierend auf psychologischer Literatur stellen wir Automation Bias als ein Phänomen dar, das mit Aufmerksamkeits- und Vertrauensprozessen sowie mit gefühlter Verantwortlichkeit zusammenhängt. Zugleich beschreiben dieser Beitrag technische, menschliche und kontextuelle Einflussfaktoren auf diese psychologischen Prozesse, welche die Wahrscheinlichkeit von Automation Bias verstärken oder abschwächen können. Im letzten Teil des Beitrages stellen wir die Beziehung zwischen den psychologischen Perspektiven des Automation Bias und der richterlichen Unabhängigkeit her.

Climate change and Artificial Intelligence (AI) are typically treated as separate regulatory domains. This article argues that they pose similar challenges to law: both are global and collectively caused, both generate diffuse and potentially long-term harms alongside highly concentrated private gains, and both strain an individual‑rights-centred legal archi- tecture designed for bilateral disputes between identifiable actors. First, I situate climate governance and AI regulation within a shared “tragedy of the commons” logic and show how existing fundamental‑rights frameworks produce a structural protection gap for systemic risks. Second, I analyse key instruments of EU environmental law, demonstrating that AI’s ecological footprint—energy and water use, resource ex- traction, rebound effects—is regulated only indirectly and that responsibility is diffused along global supply chains. I then argue that what I call “regulatory solutionism” is a particular risk in digital and climate regulation: dense process obligations obscure the absence of hard distributive choices, prohibitions, and enforceable limits. Finally, I propose us- ing the precautionary principle as a common normative foundation for climate governance and AI regulation, including throughconsumptioncapsforparticularlyresource-intensiveAIsystemsandmandatoryenvironmentalriskassessments

This special issue is the final publication of my project "Trial and Error: Experimental Regulation of Digital Technologies within the multi-layered regulatory system", funded by the Daimler and Benz Stiftung. I cannot imagine a better forum than the Cambridge Forum on AI: Law and Governance, which gave

This paper examines the legal implications of the explicit mentioning of automation bias (AB) in the Artificial Intelligence Act (AIA). The AIA mandates human oversight for high-risk AI systems and requires providers to enable awareness of AB, i.e., the human tendency to over-rely on AI outputs. The paper analyses the embedding of this extra-juridical concept in the AIA, the asymmetric division of responsibility between AI providers and deployers for mitigating AB, and the challenges of legally enforcing this novel awareness requirement. The analysis shows that the AIA’s focus on providers does not adequately address design and context as causes of AB, and questions whether the AIA should directly regulate the risk of AB rather than just mandating awareness. As the AIA’s approach requires a balance between legal mandates and behavioural science, the paper proposes that harmonised standards should reference the state of research on AB and human-AI interaction, holding both providers and deployers accountable. Ultimately, further empirical research on human-AI interaction will be essential for effective safeguards.

The purpose limitation principle goes beyond the protection of the individual data subjects: it aims to ensure transparency, fairness and its exception for privileged purposes. However, in the current reality of powerful AI models, purpose limitation is often impossible to enforce and is thus structurally undermined. This paper addresses a critical regulatory gap in EU digital legislation: the risk of secondary use of trained models and anonymised training datasets. Anonymised training data, as well as AI models trained from this data, pose the threat of being freely reused in potentially harmful contexts such as insurance risk scoring and automated job applicant screening. We propose shifting the focus of purpose limitation from data processing to AI model regulation. This approach mandates that those training AI models define the intended purpose and restrict the use of the model solely to this stated purpose.

Generative artificial intelligence (AI) has catapulted into the legal debate through the popular applications ChatGPT, Bard, Dall-E and others. While the predominant focus has hitherto centred on issues of copyright infringement and regulatory strategies, particularly in the context of the AI Act, a critical but often overlooked issue lies in the friction between generative AI and data protection laws. The rise of these technologies highlights unresolved tension between safeguarding fundamental protection rights and and the vast, almost universal, of scale of data processing required for machine learning. Large language models, which scrape nearly the whole Internet rely on and may even generate personal data falling under the GDPR. This tension manifests across multiple dimensions, encompassing data subjects’ rights, the foundational principles of data protection and the fundamental categories of data protection. Drawing on ongoing investigations by data protection authorities in Europe, this paper undertakes a comprehensive analysis of the intricate interplay between generative AI and data protection within the European legal framework.

This legal article deals with the concept of regulatory sandboxes as part of experimental regulation to meet the challenges of digitalisation for legal regimes. It focuses on a critical analysis of the new provisions of the AI Act and their weaknesses in relation to the objectives of regulatory sandboxes to promote innovation and regulatory learning. Finally, suggestions for improvement and future areas of application are outlined.