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.
By Alice Saito, Harold Godsoe, Phan Xuan Tan
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.
By Mari Reisj{\aa}, Anders Sundnes L{\o}vlie
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.
By Jerome Marston, Tino Kreutzer, Salom\'e Garnier, Ella Boone, Phuong N Pham, Patrick Vinck
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.
By Sumaya Nur Adan, Joanna Wiaterek, Nourhan Abdelrhim, Sydney Reis, Robert F. Trager, Benjamin Rosman, Marina Jirotka
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.
By Cong Chi Nguyen, Trang Mai Xuan, Vu-Duc Ngo, Kim-Ngan Thi Nguyen, Trong-Nghia Nguyen, Thien Van Luong