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Canada's Carney pushes'new alliance' with EU despite Trump threats Canadian Prime Minister Mark Carney greets European Commission President Ursula von der Leyen after delivering a speech at the European Parliament in Strasbourg, France, on Thursday. Brussels - Canadian Prime Minister Mark Carney shrugged off a threat of U.S. retaliation Thursday as he pitched a "new alliance" with Europe as a bulwark against outside pressures, in a closely watched speech to the European Parliament. His speech came after EU chief Ursula von der Leyen suggested Canada could become the 27-nation bloc's first "associate member" in her annual State of the Union address on Wednesday. "Europe and Canada are stronger together," Carney said, in a speech that drew a standing ovation, saying his country welcomed the EU proposal and wanted deeper cooperation on issues from artificial intelligence to defense and energy. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

EU chief von der Leyen proposes'associate member' status for Canada Share EU chief von der Leyen proposes'associate member' status for Canada on social media European Commission President Ursula von der Leyen has raised the prospect of Canada becoming the 27-nation European Union's first "associate member", a striking overture as both seek to pivot away from a hostile United States under President Donald Trump. Von der Leyen's statement on Wednesday came after Prime Minister Mark Carney said earlier this week that Canada was seeking a "unique alliance" with the EU, but not membership. "I would like to work with you on opening the door for Canada to be the first associate member of the EU," von der Leyen told Carney, who was attending her annual keynote speech. The first foreign head of government to attend the annual "State of the European Union" address, his visit comes in advance of an EU-Canada summit in Montreal next month. Carney will deliver his own address to the European Parliament on Thursday.

Companies will once again be allowed to scan citizens' personal texts, emails, and social media messages via the "chat control" bill to find child abuse material online. The European Parliament has voted to extend legislation allowing tech companies to voluntarily scan users' private messages for child sexual abuse material, despite a majority of lawmakers voting against the proposal. The ruling reinstates permissions for firms including Meta, Google, and Microsoft to scan private text, email, and social media messages through a bill nicknamed "Chat Control" by critics. End-to-end encrypted chats, such as those on WhatsApp and Signal, remain exempt. "It will mean that private companies may deny your right to have confidential digital conversations," Simeon de Brouwer, policy advisor at Brussels-based advocacy group European Digital Rights tells WIRED, "they could, if they want to, read every message you write, every email you send, every picture you share."

Age Verification Is Reaching a Global Tipping Point. Is TikTok's Strategy a Good Compromise? TikTok's new age-detection tech seems like a better solution than automatically banning youth accounts. But experts say it still requires social platforms to surveil users more closely. Governments worldwide are moving to limit children's access to social media as lawmakers question whether platforms are capable of enforcing their own minimum age requirements.

We present ParlAI Vote, an interactive web platform for exploring European Parliament debates and votes, and for testing LLMs on vote prediction and bias analysis. This web system connects debate topics, speeches, and roll-call outcomes, and includes rich demographic data such as gender, age, country, and political group. Users can browse debates, inspect linked speeches, compare real voting outcomes with predictions from frontier LLMs, and view error breakdowns by demographic group. Visualizing the EuroParlVote benchmark and its core tasks of gender classification and vote prediction, ParlAI Vote highlights systematic performance bias in state-of-the-art LLMs. It unifies data, models, and visual analytics in a single interface, lowering the barrier for reproducing findings, auditing behavior, and running counterfactual scenarios. This web platform also shows model reasoning, helping users see why errors occur and what cues the models rely on. It supports research, education, and public engagement with legislative decision-making, while making clear both the strengths and the limitations of current LLMs in political analysis.

Organizations investing in artificial intelligence face a fundamental challenge: traditional return on investment calculations fail to capture the dual nature of AI implementations, which simultaneously reduce certain operational risks while introducing novel exposures related to algorithmic malfunction, adversarial attacks, and regulatory liability. This research presents a comprehensive financial framework for quantifying AI project returns that explicitly integrates changes in organizational risk profiles. The methodology addresses a critical gap in current practice where investment decisions rely on optimistic benefit projections without accounting for the probabilistic costs of AI-specific threats including model drift, bias-related litigation, and compliance failures under emerging regulations such as the European Union Artificial Intelligence Act and ISO/IEC 42001. Drawing on established risk quantification methods, including annual loss expectancy calculations and Monte Carlo simulation techniques, this framework enables practitioners to compute net benefits that incorporate both productivity gains and the delta between pre-implementation and post-implementation risk exposures. The analysis demonstrates that accurate AI investment evaluation requires explicit modeling of control effectiveness, reserve requirements for algorithmic failures, and the ongoing operational costs of maintaining model performance. Practical implications include specific guidance for establishing governance structures, conducting phased validations, and integrating risk-adjusted metrics into capital allocation decisions, ultimately enabling evidence-based AI portfolio management that satisfies both fiduciary responsibilities and regulatory mandates.

In this work, we investigate the use of Large Language Models (LLMs) within a graph-based Retrieval Augmented Generation (RAG) architecture for Energy Efficiency (EE) Question Answering. First, the system automatically extracts a Knowledge Graph (KG) from guidance and regulatory documents in the energy field. Then, the generated graph is navigated and reasoned upon to provide users with accurate answers in multiple languages. We implement a human-based validation using the RAGAs framework properties, a validation dataset comprising 101 question-answer pairs, and domain experts. Results confirm the potential of this architecture and identify its strengths and weaknesses. Validation results show how the system correctly answers in about three out of four of the cases (75.2 +- 2.7%), with higher results on questions related to more general EE answers (up to 81.0 +- 4.1%), and featuring promising multilingual abilities (4.4% accuracy loss due to translation).

The development of Artificial Intelligence (AI), including AI in Science (AIS), should be done following the principles of responsible AI. Progress in responsible AI is often quantified through evaluation metrics, yet there has been less work on assessing the robustness and reliability of the metrics themselves. We reflect on prior work that examines the robustness of fairness metrics for recommender systems as a type of AI application and summarise their key takeaways into a set of non-exhaustive guidelines for developing reliable metrics of responsible AI. Our guidelines apply to a broad spectrum of AI applications, including AIS.

Large language models (LLMs) are increasingly deployed as agents in various contexts by providing tools at their disposal. However, LLM agents can exhibit unpredictable behaviors, including taking undesirable and/or unsafe actions. In order to measure the latent propensity of LLM agents for taking illegal actions under an EU legislative context, we introduce EU-Agent-Bench, a verifiable human-curated benchmark that evaluates an agent's alignment with EU legal norms in situations where benign user inputs could lead to unlawful actions. Our benchmark spans scenarios across several categories, including data protection, bias/discrimination, and scientific integrity, with each user request allowing for both compliant and non-compliant execution of the requested actions. Comparing the model's function calls against a rubric exhaustively supported by citations of the relevant legislature, we evaluate the legal compliance of frontier LLMs, and furthermore investigate the compliance effect of providing the relevant legislative excerpts in the agent's system prompt along with explicit instructions to comply. We release a public preview set for the research community, while holding out a private test set to prevent data contamination in evaluating upcoming models. We encourage future work extending agentic safety benchmarks to different legal jurisdictions and to multi-turn and multilingual interactions. We release our code on \href{https://github.com/ilijalichkovski/eu-agent-bench}{this URL}.