Why are eight US states suing OpenAI over ChatGPT safety?

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1. 1

   Governing AI for Mental Health: Fragmented State Approaches and the Case for a Federal Framework.

   JMIR mental health (Aldhalimi)Published Jul 28, 2026Checked Oct 16, 2026

   “This Viewpoint aims to identify the governance approaches that US states are using to regulate the use of AI in mental health and analyze the limitations of each. A 4-state case analysis was conducted using the statutory text of bills and laws in Illinois, Utah, New York, and Nevada. Two governance approaches were identified. The first regulates the use of AI in clinical contexts, and the second regulates the technology itself. Some states have combined elements of both approaches to address AI use more comprehensively. While these approaches aim to mitigate harm, they differ in where they believe risk lies in the use of AI for mental health support. The limitations of these divergent approaches include uneven protections for consumers and regulatory uncertainty for developers, vendors, deployers, and clinicians. Because AI in mental health operates across both clinical and consumer domains, neither approach alone can address the risks associated with its use for mental health support. A coordinated, risk-based federal regulatory floor is needed to ensure consistent protections across states.”

2. 2

   Mass Media Narratives of Psychiatric Adverse Events Associated With Generative AI Chatbots: Rapid Scoping Review.

   JMIR mental health (Chung et al.)Published Mar 30, 2026Checked Oct 16, 2026

   “Causal attribution most commonly referenced AI system behavior (45/61, 73.8%), and the term "alleged" was the predominant causal descriptor (33/61, 54.1%). Evidence sources were primarily chat logs or screenshots (34/61, 55.7%), while police or medical documentation was rare (1/61, 1.6%). Regulatory calls were present in 51 of 60 (85%) articles with nonmissing data.ConclusionsMass media reporting of generative AI-related psychiatric harms is concentrated around severe outcomes, particularly suicide deaths among youth, and is frequently framed within regulatory and corporate accountability narratives. While causality cannot be established from media reports, consistent patterns of high-intensity interactions, user vulnerability, and limited safeguard reporting highlight the need for structured safety surveillance, transparent AI risk auditing, and clearer governance frameworks. As generative AI becomes increasingly integrated into everyday psychosocial contexts, systematic research and formal safety monitoring will be necessary to determine whether media-reported harms correspond to verifiable clinical risk patterns.”

3. 3

   Safeguarding human values: rethinking US law for generative AI's societal impacts.

   AI and ethics (Cheong et al.)Published May 7, 2024Checked Oct 16, 2026

   “Constitutional and civil rights law currently struggles to hold AI companies responsible for AI-assisted discriminatory outputs. Moreover, even without considering the liability shield provided by Section 230, existing liability laws may not effectively remedy unintentional and intangible harms caused by AI systems. Demonstrating causal links for liability claims such as defamation or product liability proves exceptionally difficult due to the intricate and opaque nature of these systems. To effectively address these unique and evolving risks posed by generative AI, we propose a "Responsible AI Legal Framework" that adapts to recognize new threats and utilizes a multi-pronged approach. This framework would enshrine fundamental values in legal frameworks, establish comprehensive safety guidelines, and implement liability models tailored to the complexities of human-AI interactions. By proactively mitigating unforeseen harms like mental health impacts and privacy breaches, this framework aims to create a legal landscape capable of navigating the exciting yet precarious future brought forth by generative AI technologies.”

4. 4

   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.”

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.”