arXiv:2608. 05418v1 Announce Type: new Abstract: AI tools are being increasingly adopted in policing in the UK and worldwide.
arXiv:2603. 11001v3 Announce Type: replace-cross Abstract: Human uplift studies, or studies that measure the effects of AI access on human performance via randomized controlled trials (RCT) or similar methodologies, increasingly inform frontier AI governance and deployment decisions.
arXiv:2607. 25648v1 Announce Type: cross Abstract: Public services face growing pressure to adopt artificial intelligence (AI) to close the gap between rising demand and falling resources.
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...
arXiv:2508. 07872v2 Announce Type: replace-cross Abstract: Uncertainty in artificial intelligence (AI) predictions raises pressing legal and ethical questions for AI-assisted decision-making.
arXiv:2609.14796v1 Announce Type: new Abstract: The threat that AI persuasion poses to human control has been acknowledged in the literature, but not yet systematically studied. Now that persuasion a...
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.
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:2609.13174v1 Announce Type: cross Abstract: California recently required all prosecutors in the state to conduct a "race-blind charging" decision by reviewing case documents in which selected r...
arXiv:2606. 13039v1 Announce Type: cross Abstract: The UK government has adopted a pro-AI stance to help transform public service delivery in the face of severe financial pressures, but the path to translate this vision into responsible AI practice remains ill-defined.
arXiv:2603.19042v5 Announce Type: replace Abstract: The integration of artificial intelligence (AI) into judicial decision making -- particularly in pretrial, sentencing, and parole contexts -- has g...
arXiv:2508. 09219v3 Announce Type: replace-cross Abstract: Recent advances in AI applications have raised growing concerns about the need for ethical guidelines and regulations to mitigate the risks posed by these technologies.
arXiv:2607. 15480v1 Announce Type: new Abstract: As artificial intelligence (AI) systems increasingly impact society, ensuring their ethical and trustworthy deployment has become a global priority.