The rise of Large Language Models (LLMs) offers transfor-mative potential for interpreting complex legal frameworks, such as Title 18 Section 175 of the US Code, which governs biological weapons. These systems hold promise for advancing legal analysis and compliance monitoring in sensitive domains. However, this capability comes with a troubling contradiction: while LLMs can analyze and interpret laws, they also demonstrate alarming vulnerabilities in generating unsafe outputs, such as actionable steps for bioweapon creation, despite their safeguards. To address this challenge, we propose a methodology that integrates knowledge graph construction with Retrieval-Augmented Generation (RAG) to systematically evaluate LLMs' understanding of this law, their capacity to assess legal intent (mens rea), and their potential for unsafe applications. Through structured experiments, we assess their accuracy in identifying legal violations, generating prohibited instructions, and detecting unlawful intent in bioweapons-related scenarios. Our findings reveal significant limitations in LLMs' reasoning and safety mechanisms, but they also point the way forward. By combining enhanced safety protocols with more robust legal reasoning frameworks, this research lays the groundwork for developing LLMs that can ethically and securely assist in sensitive legal domains--ensuring they act as protectors of the law rather than inadvertent enablers of its violation.

Housing cooperative is a common type of multifamily building ownership in Sweden. Although this ownership structure grants decision-making autonomy, it places a burden of responsibility on cooperative's board members. Most board members lack the resources or expertise to manage properties and their energy consumption. This ignorance presents a unique challenge, especially given the EU directives that prohibit buildings rated as energy classes F and G by 2033. Conversational agents (CAs) enable human-like interactions with computer systems, facilitating human-computer interaction across various domains. In our case, CAs can be implemented to support cooperative members in making informed energy retrofitting and usage decisions. This paper introduces a Conversational agent system, called SPARA, designed to advise cooperatives on energy efficiency. SPARA functions as an energy efficiency advisor by leveraging the Retrieval-Augmented Generation (RAG) framework with a Language Model(LM). The LM generates targeted recommendations based on a knowledge base composed of email communications between professional energy advisors and cooperatives' representatives in Stockholm. The preliminary results indicate that SPARA can provide energy efficiency advice with precision 80\%, comparable to that of municipal energy efficiency (EE) experts. A pilot implementation is currently underway, where municipal EE experts are evaluating SPARA performance based on questions posed to EE experts by BRF members. Our findings suggest that LMs can significantly improve outreach by supporting stakeholders in their energy transition. For future work, more research is needed to evaluate this technology, particularly limitations to the stability and trustworthiness of its energy efficiency advice.

Machine Unlearning (MU) has emerged as a promising approach to addressing persistent challenges in Machine Learning (ML) systems. By enabling the selective removal of learned data, MU introduces protective, corrective, and adaptive capabilities that are central to advancing Responsible and Adaptive AI. However, despite its growing prominence in other domains, MU remains underexplored within education, a sector uniquely characterized by sensitive learner data, dynamic environments, and the high-stakes implications of algorithmic decision-making. This paper examines the potential of MU as both a mechanism for operationalizing Responsible AI principles and a foundation for Adaptive AI in ML-driven educational systems. Drawing on a structured review of 42 peer-reviewed studies, the paper analyzes key MU mechanisms and technical variants, and how they contribute to the practical realization of Responsible and Adaptive AI. Four core intervention domains where MU demonstrates significant promise are identified: privacy protection, resilience to adversarial or corrupted data, fairness through bias mitigation, and adaptability to evolving contexts. Furthermore, MU interventions are mapped to the technical, ethical, and pedagogical challenges inherent in educational AI. This mapping illustrates the role of MU as a strategic mechanism for enhancing compliance, reinforcing ethical safeguards, and supporting adaptability by ensuring that models remain flexible, maintainable, and contextually relevant over time. As a conceptual contribution, the paper introduces MU4RAAI, a reference architecture integrating MU within Responsible and Adaptive AI frameworks for educational contexts. MU is thus positioned not merely as a data deletion process but as a transformative approach for ensuring that educational AI systems remain ethical, adaptive, and trustworthy.

The Political Compass Test (PCT) and similar surveys are commonly used to assess political bias in auto-regressive LLMs. Our rigorous statistical experiments show that while changes to standard generation parameters have minimal effect on PCT scores, prompt phrasing and fine-tuning individually and together can significantly influence results. Interestingly, fine-tuning on politically rich vs. neutral datasets does not lead to different shifts in scores. We also generalize these findings to a similar popular test called 8 Values. Humans do not change their responses to questions when prompted differently (``answer this question'' vs ``state your opinion''), or after exposure to politically neutral text, such as mathematical formulae. But the fact that the models do so raises concerns about the validity of these tests for measuring model bias, and paves the way for deeper exploration into how political and social views are encoded in LLMs.

OpenAI accuses The New York Times of wanting to invade user privacy after the newspaper demanded access to 20 million private ChatGPT conversations in its ongoing lawsuit.

Recent memoirs by the Justices reveal how a new vision of restraint has led to radical outcomes. To understand how grudging Amy Coney Barrett's new book is when it comes to revealing personal details, consider that one of the family members the Supreme Court Justice most often refers to is a great-grandmother who died five years before she was born. On Barrett's desk at home, she recounts in " Listening to the Law," she keeps a photograph of her great-grandmother's one-story house, where, as a widow during the Great Depression, she raised some of her thirteen children and took in other needy relatives. "Looking at the photo reminds me of a woman who stretched herself beyond all reasonable capacity," Barrett explains. "I'm not sure that I'll be able to manage my life with the same grace that she had. But she motivates me to keep trying." For Barrett, the mother of seven children, that effort entails setting her alarm for 5 "Our kids get up at six thirty during the school year, so I start early if I want to accomplish anything on my own to-do list," she writes. This is what passes for disclosure from Barrett; she measures out the details of her life with coffee spoons, careful not to spill.

Sam Altman says the one-person billion-dollar company is coming. Maybe I could be that person--if only I could get my colleagues to shut up and stop lying. One day a couple months ago, in the middle of lunch, I glanced at my phone and was puzzled to see my colleague Ash Roy calling. In and of itself it might not have seemed strange to get a call from Ash: He's the CTO and chief product officer of HurumoAI, a startup I cofounded last summer. We were in the middle of a big push to get our software product, an AI agent application, into beta. There was plenty to discuss. "Hey there," he said, when I picked up. He was calling, he said, because I'd requested a progress report on the app from Megan. "I've been good," I said, chewing my grilled cheese.

The Munich court found that OpenAI, the maker of ChatGPT, was not entitled to use song lyrics to train its artificial intelligence without licenses, and that the artists who wrote them are entitled to compensation. The Munich court found that the maker of ChatGPT was not entitled to use song lyrics to train its artificial intelligence without licenses, and that the artists who wrote them are entitled to compensation. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right. With your current subscription plan you can comment on stories.

Volker Turk, United Nations high commissioner for human rights, attends the Human Rights Council in Geneva on Sept. 8. | REUTERS Geneva - A few tech giants accumulating massive power coupled with artificial intelligence is posing huge global rights challenges and needs regulation, the U.N. human rights chief said in an interview. Amid increasing worries over threats to democracy and with a growing number of countries at risk of sliding towards autocracy, Volker Turk said a key concern was the seeming unbridled power of a small number of technology companies. In an interview this week at the UN rights office overlooking Lake Geneva, he pointed to how seven or eight big tech companies now boast more wealth than the entire economies of even industrialized nations. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

The UK government will allow tech firms and child safety charities to proactively test artificial intelligence tools to make sure they cannot create child sexual abuse imagery. An amendment to the Crime and Policing Bill announced on Wednesday would enable authorised testers to assess models for their ability to generate illegal child sexual abuse material (CSAM) prior to their release. Technology Secretary Liz Kendall said the measures would ensure AI systems can be made safe at the source - though some campaigners argue more still needs to be done. It comes as the Internet Watch Foundation (IWF) said the number of AI-related CSAM reports had doubled over the past year. The charity, one of only a few in the world licensed to actively search for child abuse content online, said it had removed 426 pieces of reported material between January and October 2025.