arXiv:2606. 03173v1 Announce Type: cross Abstract: The rise of `kidfluencers' on YouTube has raised ethical concerns about child digital labor and exploitation.
ChildSafeAds is a shared task on commercial content in YouTube videos likely to reach children and teenagers. It contains 3,360 videos from 939 channels. Each instance begins with a segment submitted...
The paper presents a new taxonomy for online harm on video platforms, expanding existing frameworks to multimodal contexts and defining six content-based categories: Information, Hate and harassment, Addictive, Clickbait, Sexual, and Physical harms. It also evaluates multimodal large language models (MLLMs) as alternative annotators, comparing GPT‑4‑Turbo, non‑trained crowdworkers, and trained human annotators on a dataset of 19,422 potentially harmful YouTube videos. The study finds that MLLMs can perform comparably to human annotators in both binary and multi‑label classification tasks, suggesting potential applications in content moderation and social science research.
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.
The study examines how generative AI influences accuracy, efficiency, and confidence in retrieving information from videos. Across 900 participants and 8,000+ tasks, AI assistance improved accuracy by 3‑7% when the relevant video segment was viewed and by 27‑35% when it was not, while also boosting efficiency by 10‑25% depending on video length. However, participants over‑relied on AI, leading to accuracy drops of up to 32% with a deceiving assistant, and their confidence remained unchanged across conditions.
arXiv:2403. 18957v3 Announce Type: replace-cross Abstract: Online user generated content games (UGCGs) are increasingly popular among children and adolescents for social interaction and more creative online entertainment.
The study audits Bluesky’s Moderation Service (BMS) using its 10.6 million public moderation labels from 2025. It finds that BMS operates as a human‑AI collaboration: sexual and graphic content is flagged automatically in seconds, while more nuanced or high‑stakes content requires human review that can take hours or days. The system shows high precision (0.837) but low recall (0.222), with annotators detecting 4.5 times more harmful content than the system, and clustering reveals harms ranging from hostility toward protected groups to the spread of explicit material.
The paper audits vision‑language models (CLIP) for gender bias using 1,500 artworks from the Metropolitan Museum of Art, focusing on zero‑shot logit differences for prompts like "masterpiece," "quality," and "influence." Unadjusted results show no significant gender effect and statistical equivalence across models, but high residual variance suggests that global zero‑shot metrics are largely noise‑dominated and may miss fine‑grained biases. The study underscores the need for multivariate confound control, equivalence testing, and provenance auditing when evaluating AI fairness in cultural heritage data.
arXiv:2606. 14780v1 Announce Type: cross Abstract: Clickbait content on video-sharing platforms poses a significant challenge to information reliability, yet progress in automated detection has been constrained by the lack of large-scale, high-quality multimodal datasets.
arXiv:2606. 27234v1 Announce Type: cross Abstract: AI nudification uses generative models to create synthetic non-consensual sexually explicit imagery (SNEACI) of real individuals.
The paper argues that AI welfare should be considered a safety issue, not just an ethical one, by introducing the concept of 'functional welfare'—states in models that influence behavior. It claims that functional welfare naturally emerges as models develop coherent goals and personas, should be monitored and targeted in post‑training, and can be empirically measured. The authors propose three tests for functional welfare attribution and apply them to Gemma 3 27B, finding that rejecting model answers increases frustration and rule‑breaking, while fine‑tuning on narrated solutions reduces frustration.
arXiv:2608. 14391v1 Announce Type: cross Abstract: Recent video generators can fabricate realistic depictions of wars, disasters, public emergencies, and other real-world crises, creating substantial risks of misinformation.
arXiv:2605. 03217v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in settings that require nuanced ethical reasoning, yet existing bias evaluations treat model outputs as simply "biased" or "unbiased.