When Is Content AI-Generated Enough? Labelling Synthetic Media under the Digital Services Act and the AI Act
Authors: Marie-Therese Sekwenz
Published: 2026-09-07 16:36:16+00:00
Comment: 13 pages, 7 figures, 1 table. Accepted and presented at the Fifth European Conference on Algorithmic Fairness (ECAF 2026), Ghent, Belgium, 2-4 September 2026
AI Summary
This extended abstract analyzes the regulatory response to synthetic and manipulated media under the EU's Digital Services Act (DSA) and AI Act, focusing on transparency duties and labelling. It examines when labelling is an effective regulatory tool and when it risks being over-inclusive, under-inclusive, or ineffective, identifying four governance tensions: definitional ambiguity, interface and responsibility design, communicative effectiveness, and fairness and contestability.
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.