Modern regulatory frameworks assume that the phenomena they govern are knowable: harms can be identified, probabilities estimated and risks balanced against benefits. This article argues that this assumption becomes unstable when applied to genuinely novel technologies. Drawing on Frank Knight's distinction between risk and uncertainty, it contends that contemporary EU digital regulation - particularly the EU AI Act, alongside the Digital Services Act and the GDPR - applies the form of risk-based analysis to domains where the necessary epistemic foundations are absent. These regimes borrow models developed for pharmaceuticals and environmental regulation and extend them to technologies whose future effects remain deeply uncertain. The article traces the historical development of risk-based regulation, examining both its virtues and structural limitations. It argues that such frameworks systematically marginalize diffuse, long-term and structural harms, while delegating assessment to regulated entities thus creating incentives for conservative self-evaluation. It then explores decision-making under deep uncertainty (DMDU) as a source of alternative regulatory approaches, including adaptive and precautionary techniques. Finally, the article argues that risk assessment is inherently political, embedding normative choices that risk-based methodologies often obscure rather than resolve.
This case note examines the landmark judgement in Case C-115/24 (DrSmile), the CJEU’s first ruling specifically addressing cross-border telemedicine. The dispute centred on a hybrid orthodontic model where physical dental acts in Austria were coupled with remote monitoring and treatment planning from Germany. The Court’s decision creates a significant “digital scalpel,” legally slicing the medical act into discrete components. By interpreting Article 3(d) of Directive 2011/24/EU strictly, the Court ruled that only services provided exclusively at a distance qualify as telemedicine. This triggers a legal fiction that anchors regulatory oversight in the provider’s country-of-origin, favouring market integration. However, any physical component remains tethered to the destination state’s regulations. While providing much-needed judicial scaffolding, the author argues that this formalist division creates a clinical-legal gap. By severing indivisible therapeutic processes, the ruling risks creating liability vacuums and professional fragmentation.
While the legislative text of the Digital Fairness Act (DFA) has not yet been published, it is already clear that its effectiveness will depend as much on its enforcement design as on the content of its substantive provisions. Experience with recent instruments - including the Digital Services Act, the Digital Markets Act, the Artificial Intelligence Act and the Data Act - reveals persistent difficulties in ensuring consistent and timely enforcement across Member States. Building on the findings of the Commission's Fitness Check of EU Consumer Law on Digital Fairness, this article examines how the enforcement design developed in these legal acts can inform the institutional and procedural design of the DFA. It argues that the challenges identified by the Digital Fitness Check reflect deeper patterns of procedural, institutional and epistemic fragmentation in EU enforcement. By analysing how different enforcement designs address these tensions through mechanisms of centralisation, coordination and capacity-building, the article identifies key design choices that may shape the emerging enforcement framework of the DFA.
This article argues that platform capitalism generates a distinct legal problem that existing regulatory frameworks, designed to address disinformation and censorship, structurally fail to recognise: attention fragmentation. The commodification of attention produces a form of distraction that undermines the preconditions legal accountability itself depends on. Platform capitalism’s business model rewards engagement, and the dominant architectures reward a fragmented modality of engagement in particular, producing subjects who have internalised the logic of self-optimisation that Han identifies as psychopolitics. Through a theoretical genealogy from Debord’s externally imposed spectacle to Han’s psychopolitical internalisation, this article explains why distraction resists conventional legal intervention. Turning to environmental governance as a particularly stark illustration, the article reveals four structural vulnerabilities that make environmental regulation systematically susceptible to attention collapse: temporal mismatch, monitoring dependency, breakdown of accountability chains and asymmetric harm distribution. The article identifies possible policy interventions but emphasises their structural limits; legal reform cannot guarantee attention when the economic system profits from its commodification and fragmentation. The effectiveness of law in the age of platform capitalism depends on confronting the alignment between extractive capital’s interest in deregulation and platform capital’s dependence on engagement-driven fragmentation.
EU laws remain structurally misaligned with how algorithmic systems generate harms. This paper identifies an “EU collective redress gap” for harms to algorithmic “groups of persons.” It uses doctrinal comparison of the GDPR, the Representative Actions Directive (RAD) and the Artificial Intelligence Act (AI Act), complemented by case studies of TPC v Oracle/Salesforce and the CJEU’s Meta Platforms Ireland (C-319/20) judgment, to showcase this gap. The analysis demonstrates that GDPR remedies are ultimately bound to identifiable data subjects and (optionally) their mandates, RAD ties redress to consumers, and the AI Act, while repeatedly referring to “persons or groups of persons” in its risk-based prohibitions and obligations, outsources collective enforcement to RAD and offers only individualised complaints. On that basis, the paper conceptualises three group categories, organised, inferred and legislatively “vulnerable” groups, and identifies inferred, risk-exposed groups constructed from anonymised data as the main harm-bearers currently left without meaningful access to compensation. To close this gap, this paper proposes a guide for “group-friendly” collective redress consisting in privileging opt-out models, lowering representativeness thresholds (including digital expressions of support), importing WAMCA-style categorisation of claimants, introducing an explicit AI-specific representation right for “persons or groups of persons,” and extending collective standing to non-consumer groups.
