Labeling AI generated content challenges under European laws
When Is Content "AI-Generated Enough"? Labelling Synthetic Media under the Digital Services Act and the AI Act
Computers and Society
Summary
The problem is how to label content created or altered by artificial intelligence (AI) in a way that is clear and fair. The authors explore rules in Europe that require platforms to mark such content so people know when it is AI generated or manipulated. They point out that deciding when content is "AI-generated enough" to need labeling is tricky because of unclear definitions and technical challenges. The paper discusses how different parts of the process—from AI makers to users—share the responsibility for labeling, and highlights four main issues in making labeling effective and fair.
Digital Services ActAI Actsynthetic mediadeepfakesmachine-readable markingtransparency obligationslegal thresholdsprovenance systemsplatform interfacescontent labeling
Authors
Marie-Therese Sekwenz
Abstract
European platform and AI governance increasingly relies on transparency duties to address synthetic and manipulated media. Under the DSA, very large online platforms and search engines may use prominent markings and recipient-facing indication tools as systemic-risk mitigation measures. Under the AI Act, providers must support machine-readable marking, while deployers must disclose deepfakes and certain AI-generated or manipulated public-interest text, subject to statutory qualifications. This extended abstract examines when labelling is a meaningful regulatory response to synthetic media and when it risks becoming over-inclusive, under-inclusive, or ineffective. It argues that the central challenge is not only whether content should be labelled, but how legal thresholds, technical provenance systems, platform interfaces, and reporting practices determine when content is sufficiently generated, manipulated, or authentic-looking to trigger transparency obligations. Drawing on the emerging Article 50 AI Act implementation framework and a snapshot of the DSA Statement of Reasons database, the paper identifies four governance tensions: definitional ambiguity, interface and responsibility design, communicative effectiveness, and fairness and contestability. It conceptualises labelling as a socio-technical classification practice that distributes responsibility among AI providers, deployers, platforms, uploaders, and recipients.