The First Draft Code of Practice on Transparency of AI-Generated Content is an important and necessary step toward governing AI not merely as a technology, but as a knowledge-shaping force.

Transparency as Infrastructure — Assessing the EU’s Draft Code of Practice on AI-Generated Content

by ChatGPT-5.2

The First Draft Code of Practice on Transparency of AI-Generated Contentrepresents the European Union’s first serious attempt to operationalise Article 50(2) and (4) of the EU AI Act by translating high-level legal obligations into concrete, voluntary practices for AI providers and deployers. The document positions transparency not merely as a consumer-facing disclosure obligation, but as a systemic safeguard against deception, manipulation, and erosion of trust in digital information environments.

At its core, the draft Code seeks to ensure that AI-generated content is recognisable as such, whether through labelling, marking, or metadata-based signalling. This ambition reflects a broader European regulatory philosophy: that trust in digital systems must be engineered ex ante, rather than corrected ex post through enforcement alone. In contrast to US-style litigation-driven accountability, the EU is attempting to build a shared compliance infrastructure that can be adopted across sectors and technologies.

The Code is also notable for its multi-stakeholder origin. Developed with input from industry, academia, civil society, and Member States, it explicitly frames itself as a “foundation for further refinement” rather than a finished regulatory artefact.

This signals both humility and risk: humility in recognising the pace of technological change, and risk in potentially leaving too much discretion to market actors whose incentives may not align with long-term public trust.

For the education and research ecosystem—and especially for scholarly publishing—the Code touches on existential issues. If AI-generated text, images, data visualisations, and even synthetic research outputs circulate without reliable provenance signals, the epistemic foundations of scholarship are weakened. Conversely, if transparency obligations are implemented bluntly or asymmetrically, they risk entrenching incumbents, over-burdening smaller actors, or creating a false sense of reliability around “labelled” AI content that remains unverified in substance.

In short, the draft Code is best understood not as a compliance checklist, but as an early attempt to define epistemic governance in an AI-mediated society.

Pros and Cons of the Draft Code of Practice

✅ Pros

1. Establishes transparency as a norm, not an exception
By framing disclosure of AI-generated content as a baseline expectation, the Code helps normalise provenance awareness across platforms, sectors, and user contexts.

2. Aligns with the AI Act without over-specifying technology
The Code avoids hard-coding specific watermarking or detection technologies, preserving flexibility as technical methods evolve.

3. Recognises both providers and deployers
Responsibility is not limited to model developers; downstream deployers are also expected to implement transparency measures, which is critical in complex AI supply chains.

4. Supports democratic resilience and information integrity
Clear labelling and marking directly address risks related to deepfakes, synthetic political speech, and AI-generated disinformation.

5. Creates a basis for sector-specific extensions
The draft can be adapted for high-trust domains such as education, science, journalism, healthcare, and law—where provenance matters more than in casual entertainment contexts.

6. Encourages harmonisation across the EU internal market
A shared Code reduces fragmentation between Member States and lowers compliance uncertainty for cross-border services.

❌ Cons

1. Over-reliance on voluntary compliance
As a Code of Practice, adherence is not guaranteed. Actors most likely to cause harm (malicious or negligent deployers) are least likely to opt in.

2. Ambiguity around “meaningful” transparency
The draft does not clearly distinguish between cosmetic disclosure (“AI-assisted”) and actionable provenance(machine-readable, persistent, auditable signals).

3. Weak treatment of downstream reuse and remixing
Once AI-generated content is copied, transformed, or re-uploaded, transparency signals may be stripped—an issue insufficiently addressed in the draft.

4. Insufficient attention to high-risk knowledge domains
Scientific, educational, and scholarly contexts—where AI-generated errors can propagate into policy, medicine, or research—are not explicitly prioritised.

5. No clear linkage to enforcement or liability
The Code does not explain how transparency failures interact with sanctions under the AI Act, consumer protection law, or copyright frameworks.

6. Risk of false reassurance
Labelling content as “AI-generated” may create the impression that non-labelled content is human-verified or trustworthy, which is often not the case.

7. Limited discussion of adversarial behaviour
The draft underestimates how quickly watermarking, labelling, and detection mechanisms are likely to be circumvented or gamed.

Suggested Improvements and Additions

1. Introduce a tiered transparency model

Differentiate obligations by risk and context:

• Light-touch disclosure for entertainment and creative uses

• Strong, persistent provenance requirements for education, research, journalism, elections, and public policy

2. Mandate machine-readable provenance signals

Human-visible labels are insufficient. The Code should explicitly encourage:

• Metadata standards

• Cryptographic provenance (e.g. content credentials)

• API-level signalling for platforms, publishers, and archives

3. Address the full content lifecycle

Transparency obligations should persist across:

• Re-use

• Fine-tuning

• Dataset incorporation

• Secondary publication (including academic citation and educational materials)

4. Clarify interaction with copyright and moral rights

The Code should explicitly state how transparency intersects with:

• Authorship attribution

• Integrity of works

• Protection against misattribution of AI output to human creators

5. Create sector-specific annexes

Dedicated annexes for:

• Scholarly publishing

• Education and assessment

• News and public discourse
  would greatly increase practical relevance and uptake.

6. Link transparency to accountability

Non-compliance with the Code should have clear evidentiary consequences in enforcement actions under the AI Act and related EU laws.

7. Include red-team and audit expectations

Transparency mechanisms should be tested against:

• Removal

• Obfuscation

• Model-generated evasion
  to avoid purely symbolic compliance.

Conclusion

The First Draft Code of Practice on Transparency of AI-Generated Content is an important and necessary step toward governing AI not merely as a technology, but as a knowledge-shaping force. Its strength lies in setting shared expectations and acknowledging that trust must be designed into AI systems. Its weakness lies in leaving too much unresolved: enforcement, lifecycle integrity, and domain-specific risk.

For education, research, and scholarly publishing, transparency must evolve from labels into infrastructure—persistent, verifiable, and resistant to erosion. Without that evolution, transparency risks becoming performative rather than protective. With it, the Code could form the backbone of a European approach to AI that safeguards epistemic integrity in an era of synthetic abundance.