The study investigates how community‑led humanitarian responders in Sudan’s Emergency Response Rooms use Generative AI during armed conflict. Through surveys and interviews, it finds that responders rely on GenAI when human expertise is hard to reach, yet they warn that the technology cannot independently capture rapidly changing local conditions and may lead to harmful decisions if not verified. Risks also arise before use, such as exposure to danger from carrying devices or sharing sensitive information, and concerns that AI‑assisted reports could damage credibility with donors.
arXiv:2606. 29175v1 Announce Type: new Abstract: International humanitarian law protects civilians from direct attack unless and for such time as they take direct part in hostilities, with the ICRC's 2009 Interpretive Guidance operationalising this rule through a three-criterion cumulative test.
arXiv:2608. 10773v1 Announce Type: cross Abstract: The increasing use of Generative Artificial Intelligence (GenAI) in journalism raises concerns about possible detrimental effects both on journalism and its democratic function.
The increasing use of Generative Artificial Intelligence (GenAI) in journalism raises concerns about possible detrimental effects both on journalism and its democratic function. We explore these risks through a case study of GenAI in Norwegian Newsrooms during the 2025 parliamentary election campaign.
arXiv:2606. 26541v1 Announce Type: new Abstract: Data from affected populations are crucial for informing humanitarian response, but their value depends on timely and consistent interpretation of nuanced accounts of need.
The paper "Who Can Evaluate AI Independently? A Framework for Analysing Evaluation Infrastructure" introduces the PAAI framework—Power, Access, Adequacy, Independence—to assess who controls AI evaluation infrastructure and how that shapes independent evaluation. Applying PAAI to twelve African AI evaluation projects and three joint testing exercises of the AI Safety Institutes, the authors identify three recurring configurations: extractive collaboration, dependent innovation, and constrained independence. The study finds that African actors often provide data and expertise while external institutions retain control over infrastructure and evaluation frameworks, and that participation alone does not confer evaluative authority.
arXiv:2608. 10434v1 Announce Type: new Abstract: Machine learning-based Intrusion Detection Systems (IDS) have demonstrated superior performance in securing Unmanned Aerial Vehicle (UAV) networks.
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.
Machine learning-based Intrusion Detection Systems (IDS) have demonstrated superior performance in securing Unmanned Aerial Vehicle (UAV) networks. However, the 'black-box' nature of these models, combined with the high dimensionality of multimodal cyber-physical data, poses significant interpretability challenges.
The As-If Machine (AIM) is a multi‑agent AI system that lets participants co‑write personalized counterfactual narratives about their own lives under different policy futures. In two large experiments—one on science funding and one on satellite cybersecurity—AIM significantly increased participants’ psychological closeness to the risks compared with expert vignettes and unrelated writing, with effects ranging from 0.36 to 0.90 standard deviations. Across 23,496 human‑AI interactions, AIM provided concrete detail while participants supplied first‑person perspective and emotion, demonstrating that co‑writing with AI can make distant policy risks feel immediate and personal.
arXiv:2606. 11217v1 Announce Type: cross Abstract: The proliferation of large language models (LLMs) and autonomous AI agents has given rise to a rapidly growing methodological paradigm: "in silico" behavioral experiments.
arXiv:2608. 05656v1 Announce Type: cross Abstract: Safety risks of AI are becoming increasingly evident in human interactions with AI technologies.
arXiv:2607. 20255v1 Announce Type: cross Abstract: LLM-driven autonomous agents are reshaping offensive security.