Several legal experts have told BBC Verify that the second strike on an alleged Venezuelan drug boat by the US military was probably illegal, and would likely be considered an extrajudicial killing under international law. On Monday, the Trump administration confirmed that a follow-up strike on the boat - which has been criticised as a double tap - was ordered by US Navy Admiral Frank Bradley with the overall operation having been authorised by War Secretary Pete Hegseth. Nine people died in the first strike on the vessel and two survivors were left clinging to the burning wreckage when it was struck again, killing them, according to the Washington Post. A US official has said four missiles were used in the operation. The Trump administration has not denied there were survivors and has insisted the strikes on 2 September were in accordance with the law of armed conflict.

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Senator Ted Cruz leads efforts to pass AI amnesty through the NDAA, giving Big Tech federal preemption without rules to protect conservatives and children.

What is a Palestinian without olives? In Gaza, the world has seen the cost of a diplomacy that claims to uphold a rules-based order but applies it selectively. The United States intervened late, and only to defend an occupation the International Court of Justice (ICJ) has ruled illegal. Alongside other Western nations that built multilateral institutions, the US increasingly pursues nationalist agendas that undermine them. The hypocrisy is stark: one set of rules for Ukraine, another for Gaza.

Earlier this month, Sony AI released a dataset that establishes a new benchmark for AI ethics in computer vision models. The research behind the dataset, named Fair Human-Centric Image Benchmark (FHIBE), has been published in Nature . FHIBE is the first publicly-available, globally-diverse, consent-based human image dataset (inclusive of over 10,000 human images) for evaluating bias across a wide variety of computer vision tasks. We sat down with project lead, Alice Xiang, Global Head of AI Governance at Sony Group and Lead Research Scientist for AI Ethics at Sony AI, to discuss the project and the broader implications of this research. Could you start by introducing the project and taking us through some of the main contributions?

Charlotte Owen: 'The Lords were blown away by these brilliant women.' Charlotte Owen: 'The Lords were blown away by these brilliant women.' 'I don't take no for an answer': how a small group of women changed the law on deepfake porn For Jodie*, watching the conviction of her best friend, and knowing she helped secure it, felt at first like a kind of victory. It was certainly more than most survivors of deepfake image-based abuse could expect. They had met as students and bonded over their shared love of music. In the years since graduation, he'd also become her support system, the friend she reached for each time she learned that her images and personal details had been posted online without her consent.

Police accused of'homophobic assumptions' over victims of blackmail on Grindr Police failed to properly investigate allegations that a gang was blackmailing men on the gay dating app Grindr, the BBC can reveal. Our investigation has learned of five cases of suspected blackmail involving victims targeted on Grindr in one area, with at least four of them connected to the same gang, which remains at large. In one instance, a suspected victim killed himself 24 hours after a group of men turned up at his home demanding he hand over his new Range Rover. The Independent Office for Police Conduct (IOPC) watchdog has told Hertfordshire Police - the investigating force - to examine whether homophobic assumptions could have contributed to failures in the investigation. Hertfordshire Police said it was unable to discuss specific points about the case, which has now been reopened, but said it is committed to building and maintaining good working relationships with the LGBTQ+ communities.

Generative AI systems now mediate newsfeeds, search rankings, and creative content for hundreds of millions of users, positioning a handful of private firms as de-facto arbiters of truth. Drawing on a comparative-historical lens, this article juxtaposes the Galileo Affair, a touchstone of clerical knowledge control, with contemporary Big-Tech content moderation. We integrate Foucault's power/knowledge thesis, Weber's authority types (extended to a rational-technical and emerging agentic-technical modality), and Floridi's Dataism to analyze five recurrent dimensions: disciplinary power, authority modality, data pluralism, trust versus reliance, and resistance pathways. Primary sources (Inquisition records; platform transparency reports) and recent empirical studies on AI trust provide the evidentiary base. Findings show strong structural convergences: highly centralized gatekeeping, legitimacy claims couched in transcendent principles, and systematic exclusion of marginal voices. Divergences lie in temporal velocity, global scale, and the widening gap between public reliance and trust in AI systems. Ethical challenges cluster around algorithmic opacity, linguistic inequity, bias feedback loops, and synthetic misinformation. We propose a four-pillar governance blueprint: (1) a mandatory international model-registry with versioned policy logs, (2) representation quotas and regional observatories to de-center English-language hegemony, (3) mass critical-AI literacy initiatives, and (4) public-private support for community-led data trusts. Taken together, these measures aim to narrow the trust-reliance gap and prevent GenAI from hardcoding a twenty-first-century digital orthodoxy.

The rapid proliferation of benchmarks has created significant challenges in reproducibility, transparency, and informed decision-making. However, unlike datasets and models -- which benefit from structured documentation frameworks like Datasheets and Model Cards -- evaluation methodologies lack systematic documentation standards. We introduce Eval Factsheets, a structured, descriptive framework for documenting AI system evaluations through a comprehensive taxonomy and questionnaire-based approach. Our framework organizes evaluation characteristics across five fundamental dimensions: Context (Who made the evaluation and when?), Scope (What does it evaluate?), Structure (With what the evaluation is built?), Method (How does it work?) and Alignment (In what ways is it reliable/valid/robust?). We implement this taxonomy as a practical questionnaire spanning five sections with mandatory and recommended documentation elements. Through case studies on multiple benchmarks, we demonstrate that Eval Factsheets effectively captures diverse evaluation paradigms -- from traditional benchmarks to LLM-as-judge methodologies -- while maintaining consistency and comparability. We hope Eval Factsheets are incorporated into both existing and newly released evaluation frameworks and lead to more transparency and reproducibility.

The rise of Artificial Intelligence (AI) language technologies, particularly generative AI (GenAI) chatbots accessible via conversational interfaces, is transforming digital interactions. While these tools hold societal promise, they also risk widening digital divides due to uneven adoption and low awareness of their limitations. This study presents the first comprehensive empirical mapping of GenAI adoption, usage patterns, and literacy in Italy, based on newly collected survey data from 1,906 Italian-speaking adults. Our findings reveal widespread adoption for both work and personal use, including sensitive tasks like emotional support and medical advice. Crucially, GenAI is supplanting other technologies to become a primary information source: this trend persists despite low user digital literacy, posing a risk as users struggle to recognize errors or misinformation. Moreover, we identify a significant gender divide -- particularly pronounced in older generations -- where women are half as likely to adopt GenAI and use it less frequently than men. While we find literacy to be a key predictor of adoption, it only partially explains this disparity, suggesting that other barriers are at play. Overall, our data provide granular insights into the multipurpose usage of GenAI, highlighting the dual need for targeted educational initiatives and further investigation into the underlying barriers to equitable participation that competence alone cannot explain.