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FIND OUT MOREPartner Ela Barda comments on the EU's recent publication of an updated code of practice that is designed to help companies interpret the AI Act's transparency obligations, which highlights the challenges organisations encounter when attempting to comply with regulations that are widely expected to be rigorously enforced. Ela's comments were published in Compliance Week, 15 May 2026, and can be found here.
Companies “should not treat AI Act compliance in isolation,” said Ela Barda, partner at law firm Signature Litigation. The code expressly states that its transparency obligations sit alongside the EU’s Digital Services Act (DSA), the General Data Protection Regulation (GDPR), the EU Audiovisual Media Services Directive, the European Media Freedom Act, as well as consumer protection and intellectual property (IP) rules. For example, she said, for online platforms already subject to DSA systemic risk assessments (under Articles 34-35), this means integrating AI transparency into existing risk mitigation frameworks rather than building parallel processes.
According to Barda, the code “adds a new compliance layer on top of an already dense regulatory stack”, especially for companies falling both within the scope of “providers” and “deployers”. For example, online platforms sit at the intersection of provider and deployer obligations: they host and distribute third-party AI-generated content, may operate their own generative AI tools, and already face systemic risk obligations under the DSA since very large online platforms (VLOPs) must already use “prominent marking” to ensure that AI-generated or manipulated content resembling real persons, objects, or events is recognizable when presented on their interfaces.
Deployers can avoid disclosure of AI text if a natural or legal person holds editorial responsibility and the content has undergone human review. However, to comply with this, the code requires documented internal procedures identifying the responsible person by name, role, and contact details. For example, said Barda, a platform that permits media publishers to use AI drafting tools and then publish under the publisher’s editorial control may assume the exception applies. “However, if the publisher’s internal documentation is deficient, the exception fails and the platform may face questions about its own role in distributing unlabelled content,” she warned.
The code requires deployers to collaborate on a “best effort basis” with distributors (including platforms) to preserve icons and labels. But in practice, warned Barda, “content is routinely screenshotted, reposted, reformatted, and stripped of metadata as it moves across platforms. Maintaining label integrity across the distribution chain might be technically very difficult.”
