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Building Trust Infrastructure for Agentic AI

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AI, Privacy, and the Hidden Architecture of Harm from Inference

Lawmakers pressed European Commission officials on Wednesday on why existing rules protecting children online are not being enforced, even as the bloc moves toward new legislation setting a minimum age for social media.

The European Parliament’s Internal Market and Consumer Protection Committee 

 that brought together representatives from Meta, BEUC (the European Consumer Organization), the Center for Democracy and Technology Europe, European Digital Rights (EDRi), DOT Europe and Kids Unplugged Belgium.

The session came ahead of an expert panel report, 

, that will advise the Commission on minimum age thresholds and platform design requirements for minors.

A Commission official said nearly all EU member states called for a harmonized European approach to children's online safety at a 

, reflecting political pressure that has also driven France, Germany and Belgium to introduce national measures in recent months.

A Kids Unplugged Belgium representative, describing a coalition of tens of thousands of families across Europe and the United Kingdom, called for an EU-wide minimum age of 16 backed by effective age verification, with no parental consent exemptions. She argued commercial incentives always push platforms toward the lowest permissible threshold, making opt-out carve-outs self-defeating. She also called for social media to be treated as a product requiring safety testing before being marketed to children.

Civil society representatives took a different view. One said age verification is routinely circumvented, carries privacy risks and shifts responsibility away from platforms. On age limits specifically, EDRi argued that banning children from social media at 14 or 16 and then releasing them into the same unregulated environment at 18 — with no experience navigating it — risks leaving young people less equipped, not more. The advocacy group said the opportunity to learn should not be taken away, and that adults have a responsibility to empower minors to live in the digital world that has been built for them.

A Commission official told the committee that the EU is developing an age verification architecture intended to be privacy-preserving, acknowledging that guidelines issued a year ago "deliberately" left verification requirements vague because the technology and political readiness were not in place.

The official explicitly cited Australia's experience — including regular exchanges with the eSafety Commissioner — as the reference point for why verification now has to be resolved. "If you haven't had an effective age verification, you're missing an important tool to actually effectively enforce," the official said, describing it as something that now "has to be tackled."

But the official also flagged Australia's limits. An eSafety Commissioner 

, the official said, showed "yes, they have an effect — but it's also massive circumvention," adding that the EU needs to design its approach to avoid repeating that outcome.

Several MEPs questioned whether the Digital Services Act (DSA), which already imposes obligations on platforms to protect minors, is being adequately enforced.

MEP from Italy Brando Benifei said enforcement has been "far too slow," arguing that platforms have had years to bring their systems into compliance and have not done so. He called for stronger coordination between national regulators and the Commission and for binding timelines on enforcement outcomes.

 into addictive design features and account settings. But the official also acknowledged that regulators, despite last year’s 

, “do not see enough, and that is why we [regulators] have to enforce.”The Commission official added that enforcement action is expected following the expert panel's recommendations.

When an MEP asked whether any investigations specifically target the DSA's existing prohibition on targeted advertising to minors, the Commission did not point to any. A Commission official described the ban as "indeed a prohibition" and said the Commission is "looking at whether we should also extend it to other operators" not currently covered by the DSA.

A representative from DOT Europe, an industry coalition representing major online platforms, said its members are already deploying screen time limits, family pairing tools, age-appropriate content settings and wellbeing prompts — and argued that the EU already has "a substantial and sophisticated legal architecture" in the DSA, GDPR, AVMSD and Unfair Commercial Practices Directive that, if enforced, would go far.

Its central concern was fragmentation, given France, Germany, Belgium and other member states are introducing incompatible national age verification systems, block lists and parental consent models that they said amount to "the fragmentation engine," particularly where national regulators classify entire services as harmful to minors outside the DSA's harmonized risk framework.

DOT Europe called on the Commission to enforce existing rules first and ensure any new proposals — including the Digital Fairness Act — do not duplicate or conflict with the DSA.

They also raised a point that some member states are actively discussing how to prevent online providers from scanning private communications for child sexual abuse material, making it legally impossible for platforms to detect CSAM in messaging. "Protection of minors also includes enabling online providers to search for sexual abuse material," they said. "This is currently not possible."

The Commission is expected to propose amendments to consumer protection rules before the end of the year, targeting attention-maximizing features such as infinite scroll, non-personalized advertising defaults for minors, and influencer marketing disclosure obligations. A Commission official said the proposals would be "coherent and complementary" to the existing DSA framework.

The expert panel report due July 13 is expected to shape those proposals. Several MEPs and civil society speakers said children themselves are largely absent from the consultation process, and called for their meaningful participation in decisions that directly affect them.

In June, the European Commission published the final version of the 

Code of Practice on Transparency of AI-Generated Content

 (Code). Its release nearly completes the largest part of the Commission’s broader effort to develop supporting documents aimed at facilitating compliance with the transparency obligations under 

 before those obligations take effect. The Code is an essential part of this work, alongside the 

Guidelines on the implementation of the transparency obligations

 (Guidelines) for certain AI systems, which are currently available only in draft form.

The Code was developed by independent experts in a multi-stakeholder process coordinated by the AI Office. Although it has now been published, the Commission and the AI Board must still assess it before it can serve as a formal compliance tool. Once endorsed, signatories may rely on their adherence to the Code to satisfy the relevant transparency obligations under the AI Act, thereby relieving them of the need to demonstrate compliance by other means. Adherence does not, however, affect other obligations under the AI Act, including those that apply to high-risk AI systems and 

Article 50 will become applicable on August 2, 2026. This means that providers and deployers within its scope will be required to comply with a range of transparency obligations from that date. However, the timeline remains uncertain for providers. If adopted, the proposed 

 may extend the compliance deadline for providers already on the market from August 2, 2026, to December 2, 2026.

In addition to providers and deployers of generative AI systems, the Commission identifies other stakeholders that may consider signing the Code. These include providers of 

enerative AI models, which enable the creation of synthetic content, and providers of marking and detection solutions that support content provenance, watermarking, and the identification of AI-generated or manipulated media. 

Therefore, the Code is open not only to the providers and deployers of AI systems subject to Article 50 obligations, but also to the actors that are not directly bound by them

Adherence to the Code should not be understood as a guarantee of compliance, as competent authorities will ultimately remain responsible for assessing whether the requirements of Article 50 have been met. Nevertheless, the Code is expected to serve as the main reference point for those assessments, while providers and deployers who choose not to sign will need to demonstrate compliance through other means and may face greater scrutiny.

In addition, the signing procedure allows for partial participation. The Code consists of two separate sections—one for providers and one for deployers—which may be signed independently. The Commission encourages stakeholders to submit the completed 

 by 18:00 CET on July 22, 2026. Those who do will be included in the list of initial signatories, which will be published before the Article 50 obligations take effect. However, missing that deadline does not prevent stakeholders from joining the Code later.

Before turning to the substance of the Code, it is important to understand how it differs from the Guidelines on the implementation of the transparency obligations for certain AI systems under Article 50 of the AI Act. While the Code is a voluntary compliance tool, the Guidelines serve an interpretative function, clarifying the scope of the legal obligations and addressing issues not covered by the Code.

The Code focuses specifically on provisions 50(2), 50(4), and 50(5), whereas the Guidelines clarify the full range of requirements set out in Article 50. Those provisions concern providers and deployers of AI systems that generate or manipulate synthetic content, including deepfakes, specifically content marking obligations for providers and labeling obligations for deployers. The Guidelines, by contrast, address other transparency obligations under Article 50 that apply to different categories of AI systems, including virtual assistants, automated telephone systems, AI agents and other systems that interact directly with individuals, as well as emotion recognition and biometric categorization systems.

Compared with the first draft of the Code, which I 

 in January, the final version is considerably more detailed and operational. Whereas the initial draft focused on setting out the overall structure and commitments, the final text introduces more specific implementation measures and compliance expectations. Nevertheless, several aspects of the framework are still in progress and will likely require further clarification.

The Code establishes a comprehensive compliance framework for providers of AI systems that generate or manipulate synthetic audio, image, video, or text content (Article 50(2) and (5) AI Act). It does not prescribe a single technical solution for marking AI-generated content, as no single marking technique can currently meet all four requirements set out in Article 50(2) of the AI Act: effectiveness, interoperability, robustness, and reliability. Instead, providers are encouraged to implement layered transparency solutions that combine metadata, watermarking, content provenance mechanisms, and other tools. The focus is on whether the chosen approach can satisfactorily meet those requirements.

The framework is further built around four core commitments. First, providers are expected to implement machine-readable marking to identify AI-generated or manipulated content. Second, the Code requires providers to make detection mechanisms available so that users and relevant stakeholders can verify whether content has been generated or manipulated by AI. Third, it establishes quality requirements for marking and detection solutions – expecting providers to ensure that transparency measures are effective, reliable, robust, and interoperable across different systems and use cases. Finally, providers must establish and maintain documented compliance, cooperate on an ongoing basis with market surveillance authorities and contribute to broader efforts to advance technical standards and best practices for AI content transparency.

 it sets out how to meet the transparency obligations before common evaluation standards have fully emerged. 

In the absence of widely accepted benchmarks, providers will initially rely on internal testing methodologies and evolving industry practices to evaluate the effectiveness of their transparency measures. The Code places significant emphasis on further investment in technical solutions, standardization efforts, and industry cooperation.

Section 2 establishes a framework for the labeling of deepfakes and AI-generated or AI-manipulated published text by deployers of AI systems (Article 50(4) and (5) AI Act). This part of the Code applies to two categories of deployers. Firstly, it covers those who deploy AI systems that generate or manipulate synthetic audio, image, or video content, including deepfakes. Secondly, it applies to those who deploy AI systems that generate or manipulate text intended for publication. Text generated or manipulated by AI does not qualify as a deepfake under the AI Act.

The Code establishes two core commitments for deployers. First, it provides a framework for disclosing deepfakes and AI-generated or manipulated text through a harmonized set of EU “AI” labels or an equivalent disclosure mechanism. The Code specifies both design and placement requirements, and an EU icon for visual disclosure is 

 for use by every deployer. Deployers must ensure that disclosures are clear, accessible, and visible at the time of first exposure, encouraging platforms and other actors in the distribution chain to preserve those disclosures. Second, the Code encourages deployers to establish internal compliance processes and documentation, staff awareness and training, mechanisms for addressing missing or incorrect labels, and cooperation with market surveillance authorities.

In addition, the Code includes two qualifications that limit deployers' disclosure duty. It introduces a more flexible disclosure regime for artistic, creative, satirical, fictional, and similar works and implements the exemption in the AI Act for AI-generated or AI-assisted published text that has undergone human review or editorial control.

The Commission further recognizes that the current labeling framework is not a final solution but rather a starting point that will continue to evolve through the governance and standard-setting process. For example, there is no detailed guidance on what the flexible disclosure regime should look like for artistic, creative, satirical, and fictional works.

Although the Guidelines have not yet been finalized, stakeholders submitted their input by June 3; they may serve as a useful reference when interpreting the Code. A number of practical and legal questions remain unresolved, and the document provides helpful clarifications.

The Commission does not present the Guidelines as a final interpretation of Article 50. The Guidelines and the broader transparency framework are expected to evolve in light of technological developments, enforcement practice, stakeholder feedback, and future 

 case law, meaning that further clarification can be expected over time.

The draft guidelines contain several important clarifications. Here, I focus on those that appear to be most relevant to understanding the practical application of the Code.

First, both providers and deployers fall within the scope of the AI Act even when established outside the EU, provided that the output of the AI system is used in the Union. At the same time, actors whose role is limited to hosting, transmitting, or disseminating AI-generated content created by third parties, including online platforms, are generally not considered deployers. Individuals who merely receive or are exposed to AI-generated content without controlling the use of the underlying AI system do not fall within the scope of these obligations, although they are encouraged to preserve existing markings and labels. Publicly disseminated deepfakes relating to matters of public interest may be subject to the disclosure obligations even if they are done by an individual who is not acting as a provider or deployer in a broader sense.

The Guidelines also clarify that the transparency obligations apply broadly to both AI-generated and AI-manipulated content, including content that combines human-created and AI-generated elements. Ordinary editing activities, such as spellchecking, grammar correction, quality improvements, or format conversion, generally fall outside the scope of Article 50.

With regard to deepfakes, the key question is whether the content would falsely appear authentic or truthful; not every AI-generated or AI-modified image, audio, or video constitutes a deepfake. AI-generated videos of politicians or celebrities, AI-generated voice clones, and AI-manipulated images portraying real people in fabricated situations will most likely qualify. By contrast, clearly fictional content that would not reasonably be perceived as authentic, such as talking animals or impossible imaginary scenes, generally falls outside the definition. Routine technical modifications, such as color correction, noise reduction, compression, or minor editing, will generally not trigger the disclosure obligation, although the assessment remains context-specific.

The Guidelines clarify the interaction between the AI Act and the 

. Both address content moderation, but the actors differ. While the AI Act imposes labeling obligations 

on deployers of AI systems that generate or manipulate deepfakes

on providers of very large online platforms (VLOPs) and search engines (VLOSEs) that disseminate such content.

 In practice, deployers may rely on labeling tools provided by VLOPs and VLOSEs to comply with their obligations under Article 50(4).

Overall, although the Guidelines provide valuable clarification, a number of important concepts remain subject to case-by-case assessment, including the distinction between deepfakes and ordinary editing, the scope of artistic and analogous works, and what constitutes an appropriate form of disclosure. As a result, some legal uncertainty is likely to remain until enforcement practice develops.

Compliance with the transparency obligations under Article 50 of the AI Act is mandatory. Adherence to the Code, however, remains voluntary. Providers and deployers within the scope of Articles 50(2), (4), and (5) can sign the relevant section of the Code, and once it is endorsed, they will be able to rely on it to demonstrate compliance with those obligations. At the same time, adherence to the Code does not eliminate other obligations under the AI Act. Providers and deployers remain responsible for complying with any other applicable requirements, including obligations relating to high-risk AI systems or GPAI models.

Given the substantial fines for non-compliance that are likely under Article 50 (up to EUR 15 million or 3% of worldwide annual turnover for undertakings), adherence to the Code will be an attractive option for many providers and deployers. The Code offers a structured framework that may simplify interactions with regulators and reduce the burden of demonstrating compliance on their own.

But despite its practical value, the Code leaves several important issues unresolved. Beyond the procedural caveats—that the guidelines have not yet been finalized and the code still requires endorsement—several key concepts rely on case-by-case assessments rather than clear criteria. These include the distinction between deepfakes and ordinary editing, the scope of artistic, creative, fictional, satirical, and analogous works, and what constitutes an appropriate form of disclosure in a given context.

In addition, the Code itself acknowledges the current technological limitations of AI content transparency tools. No single marking technology can meet all the requirements of Article 50. Forensic detection mechanisms are not yet considered reliable enough, and common evaluation benchmarks have yet to emerge. Finally, significant uncertainty remains regarding enforcement. While the Code provides a framework for demonstrating compliance, much will depend on how market surveillance authorities interpret and assess compliance in practice once the obligations take effect.

In early March, the former French ambassador to Israel, Gérard Araud, 

 an extraordinary, but fake, video of missiles raining down on Tel Aviv. Buildings collapsed. Infrastructure burned. Coming days after the first Israeli and US strikes on Iran, the footage seemed to capture Tehran’s devastating reply. Hundreds of accounts shared it. Tens of thousands watched.

 of AI-generated videos that flooded X during the conflict, and it fooled an ambassador. Examples like this explain why policymakers are increasingly alarmed by visual misinformation. France’s President, Emmanuel Macron, drove the point home last year by 

 deepfakes of himself, including as a rapper and a beauty influencer, to promote the Paris AI Action Summit.

But the obsession with deepfakes misses the bigger threat. A 

 from the International Panel on the Information Environment (IPIE), a Swiss-based scientific consortium of more than 500 researchers across 75 countries, finds that the most immediate and persuasive risk from generative AI is not visual. It is textual. The conclusion draws on 60 effect estimates from 24 peer-reviewed randomized controlled trials involving more than 33,000 participants.

The trend is striking. People are becoming harder to fool with AI-generated images. The cruder fakes give themselves away: the 

 of seven-fingered hands and the viral “Shrimp Jesus” have become objects of ridicule rather than belief. Recent studies show deepfakes are now judged as less credible than they were just a few years ago. Public awareness has bred a useful skepticism towards what we see online.

The opposite is happening with text. As large language models like ChatGPT have grown more sophisticated, false machine-generated text is now often perceived as 

 accurate, credible and believable than truthful information. The very tools we have learned to rely on for everyday queries are the ones we struggle to distrust when they mislead us.

This matters because text is cheap, scalable and easy to customize at industrial volumes. It is also being woven into the fabric of search, messaging and conversational interfaces — a deliberate push to embed the technology into everyday institutions before its full effects are understood. Worse, the IPIE’s findings may understate the danger. Most existing studies tested static text passages, but the conversational chatbots people increasingly rely on appear substantially more persuasive than static messages. Current estimates are likely conservative.

The message for policymakers and platforms is clear: they have been right to worry about deepfakes, but they cannot afford to overlook the quieter, more pervasive influence of AI-generated text.

The IPIE’s review points to one intervention that holds up consistently across studies: preventive corrective information. The idea is simple. Give people the knowledge and context they need 

 they encounter misleading content, not after.

In practice, this means brief explanations of how AI systems generate content, reminders that those systems can produce errors, and short prompts that nudge users to think about accuracy and credibility. Across the studies reviewed, these approaches deliver a small-to-moderate but reliable reduction in the perceived credibility of misinformation. The effect sizes (0.17 to 0.43 standard deviations) may sound modest, but applied at the scale of platforms with hundreds of millions of users, the real-world impact is substantial.

Content labelling is trickier. Labels flagging content as AI-generated or potentially misleading can reduce credibility, but their effects swing widely depending on design, wording, timing and source. A poorly built label can have no effect, or even backfire.

Labelling should not be abandoned. It cannot be treated as a plug-and-play fix either. It is a design problem, demanding rigorous testing, refinement and adaptation across platforms and audiences.

That cautious framing reflects a broader limitation worth confronting head-on. For all the strengths of the underlying research, the evidence base has serious gaps. It is overwhelmingly concentrated in the United States and Western Europe, leaving most of the world’s information environments effectively unstudied. Most experiments are run in controlled conditions, which may not reflect how people behave while scrolling a chaotic social feed. And the science is chasing a moving target: research cycles run in years, generative AI cycles in months. By the time a study is published, the threat it describes has often evolved.

Closing these gaps is essential if policy is to keep pace. Independent researchers need better and more timely access to platform data. Cross-regional, properly funded research must become a prerequisite for any policy claiming to work globally. Without that, public policy will remain reactive and incomplete.

The stakes are not abstract. Generative AI is already shaping public discourse from elections to public health. As these systems embed themselves further into our information ecosystems, their influence will only grow.

The question is not whether misinformation can be eliminated. It cannot, and never has been. The question is whether we can reduce its impact and harden the resilience of our information environment.

The evidence says we can. But doing so will require shifting our focus, expanding our knowledge base and investing in interventions that actually work. Above all, it requires accepting that the most significant risks are not always the most visible ones.

If we keep watching only for what we can see, we will miss what is already persuading us.

At the dawn of the internet in 1996, Congress adopted 

 of the Communications Decency Act (CDA), which generally shields online platforms from liability for content posted by their users. Europe soon followed suit. This deregulatory exemption from normal legal liability is widely considered to have made the creation of the modern internet with its many positive attributes possible. But by immunizing internet platforms it also allowed them to function as sources of disinformation, hate speech, political extremism and mental health risks for the world’s children.

Thirty years later, the reaction has set in. Countries around the world are 

 social media companies from offering their services to children. The Court of Justice of the European Union recently 

 legal protections in Europe, ruling that exemption from liability did not apply where an internet service provider uses an algorithm in respect of the information it transmits. Recent US 

 involving addictive designs and child exploitation have begun to bypass Section 230’s immunity by relying on product liability rules that punish social media companies for defective designs that harm children rather than for the content of their users’ speech.

Lawmakers should not want to repeat this mistake when it comes to AI. Lina Khan, then head of the Federal Trade Commission, 

 years ago there is no AI exemption from current law, and a useful part of any AI legislation going forward would be a ringing affirmation that AI companies are responsible for the products they produce. It is generally 

 that Section 230 does not apply to companies providing generative AI services, since Section 230 protects only user speech, not the content produced in part by AI companies through the models they provide to the public. Despite Section 230, court cases holding AI companies liable for 

 are underway, and one has resulted in a 

But judicial activity and calls for executive branch and/or state AI regulation beg the question of Congress’s role. We posit that Congress is uniquely positioned to reassure the American people that AI will be developed in accordance with existing consumer protection law, as well as protecting the public from catastrophic harms. Therefore, we propose that the US should adopt new laws affirming consumer protection law, as well as ensuring that cybersecurity, bioweapons risk and catastrophic harm are avoided. Focusing these new AI laws narrowly on catastrophic and consumer harm could incentivize broad bipartisan support, leaving for another day or different legislative vehicles other important public policy issues raised by AI including energy usage and possible impact on jobs.

The time to act is now because opposition to AI is growing in the US and because there is a competition between the US, the EU, and China on how best to regulate AI. In fact, a Spring 

 found Americans most concerned about AI compared with 24 other countries, and almost half of them had little or no trust in the US government to regulate AI effectively. Moreover, those concerns are not related to political attachment, which is very rare today.

We suggest two separate but complementary paths forward for AI legislation—one narrowly focused on AI frontier models and catastrophic risks and another focused more broadly on algorithmic decision tools and a range of consumer harms.

For example, the approach of national security and catastrophic risk reduction for frontier AI models is embodied in the bipartisan 

 introduced by Congressman Jay Obernolte (R-Calif.) and Congresswoman Lori Trahan (D-Mass.). The proposal 

 the Center for AI Standards and Innovation (CAISI) in the Commerce Department to develop voluntary standards and guidelines for frontier AI systems and provides annual appropriations $100 million for testing these systems. It also requires frontier AI developers to write and implement plans addressing risks prior to releasing new models. Like some state bills, it focuses on ensuring that AI models have been developed with “acceptable levels of mitigation of catastrophic risks.”

Senator Ron Wyden (D-Ore.) and Congresswoman Yvette D. Clarke (D-NY) have also reintroduced their 

 AI model deployers to conduct impact assessments and to attempt to eliminate or mitigate any negative impacts on consumers. A negative impact is defined broadly to include any “likely material negative impact that has legal or similarly significant effects on a consumer’s life.” The bill empowers the Federal Trade Commission to enforce these requirements.

These approaches differ in the range of AI systems they regulate and the harms they seek to prevent, but they are complementary and reinforce each other. In light of the public backlash against AI, Congress should act immediately to move both of them. Congress should also consider tightening up US export control law to ensure that administrations do not act 

 against individual companies, as the administration recently 

 in an export control order against Anthropic. But the immediate priority should be in avoiding catastrophic harm and reaffirming the applicability of consumer protection law in the AI context.

Would all this be enough to reassure the American public? Probably not. But it would be a start, especially if combined with other measures such as 

 to protect ratepayers against increases in electricity rates caused by AI data centers, and comprehensive privacy legislation. And because these measures would be laws, not Executive Orders easily rescindable at any time, they should have a useful impact on the public’s willingness to give AI another look.

Congress should make it clear to Americans that AI is not exempt from American consumer protection law. Companies remain responsible for the products they release to the public. Congress should begin its path to AI legislation by saying so.

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