Is OpenAI watermarking ChatGPT outputs in the EU, and what does the EU AI Act require?

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1. 1

   Watermarks Without Verification: AI Text Watermarking After the EU AI Act

   arXiv (Cornell University) (Nemecek et al.)Published Sep 9, 2026Checked Oct 8, 2026

   “In this work, we argue that neither the objections nor the assurances can currently be verified and that this unverifiability, rather than watermarking itself, is the substantive governance failure. We sort the contested assertions by what it would take to settle each and evaluate the open-source SynthID-Text implementation on two open-weight models, because no public tool can test the deployed systems. On prose, the measured effect of the watermark does not exceed that of changing the sampling seed. On code, the cost is three points of correctness on one model and below measurement on the other, while detection remains near chance, a limitation of detectability rather than quality. The remaining gaps trace to withheld access or missing institutions and we map each to a requirement: release of matched outputs, configuration disclosure, accredited audits, a shared evaluation protocol, and interoperable detection.”

2. 2

   Watermarking for AI Content Detection: A Review on Text, Visual, and Audio Modalities

   arXiv (Cornell University) (Cao)Published Apr 2, 2025Checked Oct 8, 2026

   “The rapid advancement of generative artificial intelligence (GenAI) has revolutionized content creation across text, visual, and audio domains, simultaneously introducing significant risks such as misinformation, identity fraud, and content manipulation. This paper presents a practical survey of watermarking techniques designed to proactively detect GenAI content. We develop a structured taxonomy categorizing watermarking methods for text, visual, and audio modalities and critically evaluate existing approaches based on their effectiveness, robustness, and practicality. Additionally, we identify key challenges, including resistance to adversarial attacks, lack of standardization across different content types, and ethical considerations related to privacy and content ownership. Finally, we discuss potential future research directions aimed at enhancing watermarking strategies to ensure content authenticity and trustworthiness. This survey serves as a foundational resource for researchers and practitioners seeking to understand and advance watermarking techniques for AI-generated content detection.”

3. 3

   Generative AI and Copyright in the European Union: Transparency, Copyright Compliance, and the AI Act

   International Journal of Law Management & Humanities (Rathee)Published Oct 6, 2026Checked Oct 8, 2026

   “It distinguishes between two questions that are often conflated: whether AI-generated or AI-assisted content deserves copyright protection, and whether the providers and deployers of AI systems that produce such content bear regulatory responsibilities. Applying doctrinal legal research to the AI Act, the study examines the treatment of general-purpose AI models, copyright-compliance policies, training-content summaries, synthetic-content transparency, documentation, institutional supervision and adaptive codes of practice. It also places these obligations alongside the Court of Justice’s originality standard, which has so far been tied to the author’s own intellectual creation expressed through free and creative choices. The study concludes that the EU framework works best in relation to provider accountability, provenance and transparency, but that some important output-side copyright issues remain to be addressed by the existing copyright framework. In that light, it suggests strengthening the links between AI law and copyright law without turning the AI Act into a copyright-specific law.”

4. 4

   Regulating ChatGPT and other Large Generative AI Models

   ACM Conference on Fairness, Accountability, and Transparency (FAccT) (Hacker et al.)Published Jun 12, 2023Checked Oct 8, 2026

   “It suggests a novel terminology to capture the AI value chain in LGAIM settings by differentiating between LGAIM developers, deployers, professional and non-professional users, as well as recipients of LGAIM output. We tailor regulatory duties to these different actors along the value chain and suggest strategies to ensure that LGAIMs are trustworthy and deployed for the benefit of society at large. Rules in the AI Act and other direct regulation must match the specificities of pre-trained models. The paper argues for three layers of obligations concerning LGAIMs (minimum standards for all LGAIMs; high-risk obligations for high-risk use cases; collaborations along the AI value chain). In general, regulation should focus on concrete high-risk applications, and not the pre-trained model itself, and should include (i) obligations regarding transparency and (ii) risk management. Non-discrimination provisions (iii) may, however, apply to LGAIM developers. Lastly, (iv) the core of the DSA's content moderation rules should be expanded to cover LGAIMs. This includes notice and action mechanisms, and trusted flaggers.”

5. 5

   Generative AI, copyright and the AI Act

   Computer law & security review (Quintais)Published Jan 29, 2025Checked Oct 8, 2026

   “The paper then explores the AI Act's structure and key definitions relevant to copyright law. The core analysis addresses the AI Act's impact on copyright, including the role of TDM in AI model training, the copyright obligations imposed by the Act, requirements for respecting copyright law—particularly TDM opt-outs—and the extraterritorial implications of these provisions. It also examines transparency obligations, compliance mechanisms, and the enforcement framework. The paper further critiques the current regime's inadequacies, particularly concerning the fair remuneration of creators, and evaluates potential improvements such as collective licensing and bargaining. It also assesses legislative reform proposals, such as statutory licensing and AI output levies, and concludes with reflections on future directions for integrating AI governance with copyright protection.”

6. 6

   Generative AI in EU law: Liability, privacy, intellectual property, and cybersecurity

   Computer law & security review (Novelli et al.)Published Oct 24, 2024Checked Oct 8, 2026

   “The complexity and emergent autonomy of Generative AI systems introduce challenges in predictability and legal compliance. This paper analyses some of the legal and regulatory implications of such challenges in the European Union context, focusing on four areas: liability, privacy, intellectual property, and cybersecurity. It examines the adequacy of the existing and proposed EU legislation, including the Artificial Intelligence Act (AIA), in addressing the challenges posed by Generative AI in general and LLMs in particular. The paper identifies potential gaps and shortcomings in the EU legislative framework and proposes recommendations to ensure the safe and compliant deployment of generative models.”

7. 7

   Duty of Care for Child Protection in AI Chatbots: A Comparative Analysis of EU, US, and Chinese Regulatory Frameworks

   Exploring Science Academic Conference Series (Xu)Published Jul 6, 2026Checked Oct 16, 2026

   “This article raises two critical questions: How do the European Union, the United States, and China regulate AI chatbots? Should these systems be recharacterized as products with design defects rather than protected speech? This study adopts a comparative legal approach to the regulatory frameworks in Europe, America, and China. The paper analyzes three particular models: the EU’s precautionary risk-based model, China’s administrative oversight, and the US’s tort-based litigation. The findings suggest that the notion of AI as a neutral tool is increasingly untenable. Traditional safe harbor protections are no longer enough to protect children. The paper presents a triple-layer framework to strengthen Duty of Care. This combines China’s pre-deployment (ex-ante) administrative filing at the input layer, the EU’s safety-by-design standards at the model layer, and the US’s post-harm (ex-post) product liability at the output layer. By establishing a triple-layer framework, regulators can effectively intervene in real time to prevent harmful manipulation of minors by AI chatbots.”

8. 8

   Human-like conversational agents as social partners: a scoping review of socioaffective mechanisms, well-being outcomes, risks and governance in the post-Turing era.

   Frontiers in artificial intelligence (Li et al.)Published Jul 15, 2026Checked Oct 16, 2026

   “We synthesized mechanisms that make agents feel social, including anthropomorphism, social presence, mind perception, self-disclosure, parasocial attachment, and socioaffective alignment. Therapeutic chatbot studies provided the strongest evidence for short-term symptom reduction in selected contexts, whereas evidence for sustained loneliness reduction in open-domain companion systems remained emerging. Reported and hypothesized risks included dependency-like use, displacement of human interaction, maladaptive validation or sycophancy, persuasive manipulation, privacy harms, and risks to minors or vulnerable users.DiscussionWe propose an integrative pathway linking model capabilities and product design cues to relational outcomes and outline a companion-specific relational safety stack as a synthesis-based evaluation agenda rather than a validated regulatory or clinical standard. The review identifies measurement priorities for evaluating companion agents and governance priorities for managing psychosocial impact as these systems scale.”