The paper "Queering the Audits: Community-Based Auditing of AI Harms to Queer Communities" reports that AI systems often embed majority-group defaults, leading to harms such as erasure, misclassification, and discrimination against queer communities. Traditional technical audits, which rely on aggregate metrics, fail to detect these harms that become apparent only through lived experience. The authors conducted a participatory auditing workshop at EurIPS 2025, where 16 queer community members used the 4Cs harm taxonomy to audit four AI case studies, tracing harms to problem definition, data collection, and contractual structures that exploit vulnerable populations.
arXiv:2608. 14692v1 Announce Type: cross Abstract: Personalized, generative AI systems increasingly adapt their behavior to individual users over time, fundamentally changing model behavior.
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.
Claims about AI safety reach audiences well beyond the AI community, yet many rely on opaque evidence or static assessments, when supporting evidence is accessible at all. We present the Systemic Risk...
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.
arXiv:2607. 05407v1 Announce Type: cross Abstract: Modern artificial intelligence (AI) systems present profound new risks to child safety.
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.
arXiv:2607. 18263v1 Announce Type: new Abstract: AI-generated non-consensual intimate imagery (AIG-NCII) is not adequately addressed in AI/ML literature regarding AI-generated media, commonly referred to as "deepfakes".
arXiv:2512.03068v2 Announce Type: replace-cross Abstract: Artificial Intelligence (AI) systems increasingly shape consequential decisions, creating value but also potential harms for individuals, soc...
arXiv:2606. 09408v1 Announce Type: cross Abstract: We present an ethnographic study of an alternative approach to data work, developed by a civic-tech initiative that builds datasets for training and benchmarking online safety systems.
NodeSynth is a method that generates socially aligned synthetic data by combining a fine‑tuned taxonomy generator with credible sources and extracted sensitive attributes to produce queries that reflect real societal contexts and nuanced harms. In evaluations across ten open‑weight and proprietary large language models on Medical Advice and Self‑Harm tasks, NodeSynth outperformed generic synthetic baselines, structured synthetic benchmarks, and authentic human‑authored queries, inducing higher failure rates that highlight model vulnerabilities. Ablation and variance attribution studies show that the granular Level 3 taxonomic expansion and sociotechnical grounding of queries drive these failure rates, underscoring the importance of nuanced data for high‑stakes evaluation.
arXiv:2608. 05418v1 Announce Type: new Abstract: AI tools are being increasingly adopted in policing in the UK and worldwide.
NodeSyn is a method that generates socially aligned synthetic data for evaluating AI models. It combines a fine‑tuned taxonomy generator with credible sources and sensitive attributes to produce queries that reflect real societal contexts and nuanced harms. In tests across ten LLMs in Medical Advice and Self‑Harm, NodeSyn outperformed generic synthetic baselines, raising failure rates significantly and revealing that failures stem from both topic selection and sociotechnical grounding.