Is USA Today's publisher suing OpenAI over training data?

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1. 1

   Umjetna inteligencija i mediji: The New York Times Company protiv kompanija OpenAI i Microsoft

   Research paper (Mešević)Published Oct 1, 2024Checked Oct 9, 2026

   “In addition, it addresses the possible safe harbours for AI-developers in this context. Firstly, in the European Union, in terms of the text and data mining exception from Directive (EU) 2019/790, which is strongly backed by the text of the EU AI Regulation. Secondly, in the United States of America, in terms of applying the legal doctrine of „fair use“, especially in the sense of the transformative use of works. The author elaborates in detail the circumstances of the origin and the content of the dispute and looks at parallel cases in the domain of relationship between copyright and artificial intelligence, which could provide some guidance regarding the direction of the outcome of the NYTC– case. In addition, the paper draws attention to positive examples of OpenAI concluding agreements and even creating partnerships with large media organizations (e.g. Associated Press, Le Monde or News Corp) regarding the use of their content in GPT-model training, as well as in the context of technological cooperation.”

2. 2

   The Lawfulness of Using Inventions for Generative AI Training : A Case Study of a US Lawsuit against OpenAI and Perplexity AI

   JUSTISI (Ismantara & Silalahi)Published Dec 25, 2024Checked Oct 9, 2026

   “GAI outputs may also infringe copyright if: (1) the source is not cited, violating Article 7 on copyright management information; (2) substantial portions of the work are reproduced, violating the rightholder's economic rights under Article 9; or (3) the work is distorted in a way that harms the rightholder’s honor, infringing on moral rights under Article 5. To accommodate AI development, specific regulations integrating AI transparency principles outlined in SE Kominfo 9/2023 are required. These regulations could include obligations for AI companies to release summaries of training datasets, include Uni EropaLAs that define the responsibilities of AI developers and users, and provide disclaimers regarding AI's limitations. Regarding the fulfillment of rightholders’ economic rights, a non-exclusive blanket license through Collective Management Organizations (CMOs) as stipulated in Permenkumham 15/2024 is necessary. These regulations should be synchronized with related policies to establish legal certainty that adapts to technological advancements.”

3. 3

   Strength in Numbers: Group Copyright Registration of Online News Articles as a Weapon in the Battle Against AI Infringement

   Utah law review (Farmer)Published Jan 1, 2026Checked Oct 9, 2026

   “In this fight, copyright protection remains an essential weapon for publishers to safeguard their content. However, U.S. copyright law has lagged behind the news industry’s integration of new technologies and mediums, with current procedures for staying abreast of copyright registration burdensome or infeasible. A new regulation from the U.S. Copyright Office, “Group Registrations of Updates to a News Website,” or 89 FR 311, allows a digital news publisher to collectively register a group of updates to a news website by submitting a representative portion of the content, rather than the complete contents of the website. It has the potential to resolve challenges standing in the way of online news publishers seeking this protection for their pages. I examine how published content is used by generative AI, current copyright protections, and how 89 FR 311 could strengthen copyright protections for news publishers while significant legal issues regarding generative artificial intelligence and the media await resolution.”

4. 4

   Assessing the feasibility of collective licensing of in-copyright works as training data for generative AI systems.

   Proceedings of the National Academy of Sciences of the United States of America (Samuelson)Published Jul 20, 2026Checked Oct 9, 2026

   “If fair use defenses succeed, developers will be free to continue to commercially exploit models already built on copyrighted data as well as to use these data to train new models or fine-tune existing ones. If copyright owners prevail, developers may be liable for billions of dollars of damages. Developers could also be enjoined from further model development on in-copyright works and even ordered to destroy models trained on infringing works. Numerous commentators have proposed collective licensing as a compromise solution to the copyright-training-data dilemma. Other commentators have questioned the feasibility of such a compromise. This article discusses several proposals for collective licensing to enable development of generative AI systems while providing some compensation to copyright owners. It assesses the complex normative, economic, and practical problems that must be addressed if such a regime is to become feasible. It discusses the implications of a licensing mandate not only for the large firms whose models are widely used today, but also for start-ups, research centers, and higher education developers of generative AI systems, as well as the general public.”

5. 5

   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.”

6. 6

   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.”

7. 7

   Legal frictions for data openness: Reflections from a case-study on re-use of the open web for AI training

   HAL (Le Centre pour la Communication Scientifique Directe) (Ramya)Published Mar 27, 2025Checked Oct 9, 2026

   “While techno-legal openness is necessary, this report argues that the political economy of data re-use also necessitates legal strategies that impose certain limits on data extractivism by well-resourced actors like Big Tech on the one hand, and enable community data sovereignty on the other hand. It contains a repository of 41 ongoing legal controversies relating to copyright and data protection related to training foundation AI models, together with a detailed analysis of how these legal controversies either impact or advance three-dimensional data openness of training datasets. It also contains a critical analysis of existing open licenses, permissive licenses, as well as certain alternative licensing frameworks for training datasets. While these licensing frameworks impose more obligations on re-users and necessitate more collective thinking on interoperability, these licensing frameworks together with other legal and institutional changes are nonetheless necessary for the creation of healthy digital and data commons, to realise the original promise of the open web as open for all.”