The paper presents a child-centered harm taxonomy for AI systems and analyzes 1,331 publicly reported child-related AI incidents. It finds that harms are concentrated in representational, dignity-related, and psychological categories, often linked to deployment-context, governance, and misuse failures rather than model defects. The study highlights that model-level evaluations alone cannot anticipate the harm pathways most frequently documented for children, and recommends broader mitigations such as age assurance, consent verification, safety-by-design UX constraints, incident disclosure norms, and targeted auditing regimes.
By Junfeng Jiao, Kevin Chen, Shrestha Mishra, Abhejay Murali, David Atkinson, Shriti Raj, Saleh Afroogh
AI companions powered by large language models increasingly interact with cognition-developing users, including children and adolescents, creating risks that may accumulate over time. Existing safety evaluations largely rely on single-turn or short-session tests, which cannot capture risks that emerge only through prolonged interaction.
How can we evaluate whether frontier AI systems recognize child-safety risks before they escalate into explicit harm? Existing child safety evaluations focus on child sexual abuse material, yet many child-safety failures begin earlier: in model assistance that helps adults manipulate, impersonate, profile, or isolate minors, and in model responses that deepen children's emotional dependence on AI systems rather than redirecting them toward human support.
The paper systematically reviews 100 empirical HCI studies on children and youth interacting with AI in various settings. Using the YAIR risk taxonomy and MIT Mitigation Taxonomy, it maps identified risks to countermeasures, finding that most risks are only matched with proposed countermeasures, few are implemented, and even fewer are evaluated. The authors highlight gaps in coverage and testing, and suggest concrete research directions to improve youth AI safety.
By Pratyasha Saha, Yaman Yu, Yang Wang
arXiv:2606. 29685v1 Announce Type: new Abstract: How can we evaluate whether frontier AI systems recognize child-safety risks before they escalate into explicit harm?
By Kaavya Krishna-Kumar, Elaine Lau, Vaughn Robinson, Jay Caldwell, Sheriff Issaka, Skyler Wang, Francisco Guzm\'an, Steven Kelling, Jonas Mueller
arXiv:2607. 09586v1 Announce Type: new Abstract: The proliferation of agentic AI systems across enterprise and public-sector contexts has outpaced the capacity of general-purpose AI risk frameworks to classify and govern them.
By Hannah M. Liu, Rhea Saxena, Shiv Asthana
arXiv:2606. 19899v1 Announce Type: cross Abstract: This paper addresses a rapidly emerging policy challenge: how to generate and interpret credible evidence about the biological capabilities and risks of AI scientists, or agentic AI systems capable of autonomously or collaboratively performing multi-step scientific tasks.
By Patricia Paskov, Jeffrey Lee, Kyle Brady, Alyssa Worland
arXiv:2607. 02197v1 Announce Type: cross Abstract: The society and emerging risk-based regulatory frameworks for AI underscore the need for rigorous risk assessment to ensure safe and reliable AI systems.
By Javier Irigoyen, Roberto Daza, Aythami Morales, Julian Fierrez, Ruben Tolosana, Ruben Vera-Rodriguez, Francisco Jurado, Alvaro Ortigosa
The paper introduces the Systemic Risk Index, an open pipeline and dashboard that aggregates evidence from 19 public AI benchmarks into four systemic‑risk categories defined by the EU GPAI Code of Practice. It evaluates 18 models using harm‑preserving perturbations and simulated deployment contexts, offering users the ability to switch between average and worst‑case aggregation and to trace each risk rating back to its benchmark evidence. The study finds that worst‑case scores can be 14 to 37 points lower than average scores, and that LLM judges agree with human graders at a level comparable to human‑human agreement.
By Jacob T. Emmerson, Phuong-Anh Nguyen-Le, Ronan Romano, Wilber Sean V. Anterola, Yann Billeter, Zhijing Jin
arXiv:2610.28430v1 Announce Type: cross
Abstract: The increasing complexity of Industrial Control Systems (ICS) and their integration with IT infrastructures has significantly expanded the cyber thre...
By Vita Santa Barletta, Danilo Caivano, Gabriel Cellammare, Andrea Dimauro, Pasquale Mesagna, Massimiliano Morga
arXiv:2607. 05407v1 Announce Type: cross Abstract: Modern artificial intelligence (AI) systems present profound new risks to child safety.
By Neil Kale, Rebecca Portnoff, Pratiksha Thaker, Michael Simpson, Robertson Wang, Kevin Kuo, Chhavi Yadav, Virginia Smith
Cognitive Digital Twins (CDTs) are dynamic computational models that represent an individual’s cognition, continuously updated with behavioral, contextual, or physiological data to predict or simulate that person’s mental processes or act as a proxy for communication and decision‑making. The paper defines CDTs, distinguishes them from related systems, and introduces a 5A governance framework—authority, autonomy, access & control, accountability, and availability—to address their unique risks. It identifies specific threats such as misrepresentation, epistemic authority shifts, shadow twins, and proxy‑power asymmetries, and proposes governance requirements for high‑risk CDTs, including stronger consent, purpose limitation, validity, traceability, contestation, independent review, and model retirement.
By Vamshi Krishna Bonagiri, Juan Nicolas Sepulveda-Arias, Abdoul Jalil Djiberou Mahamadou, Monojit Choudhury