arXiv AI
The Watchtower Imperative: Lifespan-Calibrated Evaluation for Frontier AI
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arXiv AI
5d ago
Safe by Benchmark, Unsafe in Practice: Lessons from 1331 Child-Related AI Incidents
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.
Hugging Face Trending Papers
Jun 24
Long-Term Simulation Exposes Cognitive-Developmental Risks in AI Companions
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.
Hugging Face Trending Papers
Jun 29
CAREBench: A Child-Safety Risk Benchmark for Language Models
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.
arXiv AI
Oct 7
Systemization of Knowledge (SoK): Human-Centered AI Safety for Youth
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.
arXiv Machine Learning
Jun 30
CAREBench: A Child-Safety Risk Benchmark for Language Models
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?
arXiv AI
Jul 13
TrustX Agent Risk Classification Framework (ARC): Risk-Tiering Internally Created Agentic AI Systems
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.