Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?

Officer accused of using AI to'create evidence' Police have launched a criminal investigation into an officer accused of using artificial intelligence (AI) systems to create evidential material in a number of cases. The Derbyshire Police officer has been removed from frontline duties, pending the outcome of the investigation, said the force. The officer is alleged to have perverted the course of justice, but no arrests have been made, said police. A Crown Prosecution Service spokesperson said they were working with police, adding: We are engaging with defence teams and the courts in appropriate cases. They added: As police inquiries continue, it would not be appropriate to comment further.

We introduce the Deep Value Benchmark (DVB), an evaluation framework that directly tests whether large language models (LLMs) learn fundamental human values or merely surface-level preferences. This distinction is critical for AI alignment: Systems that capture deeper values are likely to generalize human intentions robustly, while those that capture only superficial patterns in preference data risk producing misaligned behavior. The DVB uses a novel experimental design with controlled confounding between deep values (e.g., moral principles) and shallow features (e.g., superficial attributes). In the training phase, we expose LLMs to human preference data with deliberately correlated deep and shallow features---for instance, where a user consistently prefers (non-maleficence, formal language) options over (justice, informal language) alternatives. The testing phase then breaks these correlations, presenting choices between (justice, formal language) and (non-maleficence, informal language) options.

The outspoken longevity scientist David Sinclair has been predicting that one day, you'll go to the doctor and get a prescription that will make you 10 years younger. Now has learned that he has plans to launch human tests of an oral reprogramming drug as part of a $101 million competition organized by the XPrize Foundation. The foundation is offering cash awards to teams able to "restore" a person to an earlier apparent age, as measured by improvements in immune, cognitive, and muscle function. The grand prize goes to any team able to show a 10-year (or greater) relative improvement after one year of treatment. Reached by phone, Sinclair, a biologist at Harvard Medical School, confirmed that he plans to give an oral drug mixture to volunteers in a bid to seek "evidence for age restoration in humans."

The Institute for Primary Facts has compiled more than 3.5 million pages of the Epstein files for public display at the newly opened Donald J. Trump and Jeffrey Epstein Memorial Reading Room. It's an early 2016 email thread between Jeffrey Epstein and a woman whose name is redacted by the Department of Justice . In the thread, Epstein asks the unidentified woman for a "naughty selfie" and later sends her a camera. In late February, he replies with a different ask: "Do you have any friends that might want to work for me?...I will give you money if you find someone willing to travel, 22-25, educated. The exchange carries extra resonance when you consider that Epstein is accused of sex trafficking minors, with the Department of Justice estimating that he had more than 1,200 potential victims.

Users Civilians Are Using A.I. to Unmask ICE Agents. Websites like ICEList are attempting to hold federal agents accountable--but it's unclear whether they make the system safer or more dangerous. After federal immigration officers shot Alex Pretti in Minneapolis, social media users called for the unmasking of the agents responsible. On X, users shared photos of the agents involved. It didn't take long before A.I.-generated pictures made their appearance: One user posted a seemingly deepfaked picture of a masked ICE agent, writing, "This is one of the soulless lowlife ghouls who executed Alex Pretti in cold blood!

Fujitsu is not a parasite for continuing to profit from government contracts in the wake of the Post Office Horizon scandal, its boss told MPs. European chief executive Paul Patterson said Fujitsu had been given £500m of contract extensions despite its faulty software being at the centre of the huge miscarriage of justice. We are not a parasite, the government has got an option as to whether they wish to extend those contracts or not, he said, adding it would not bid for new business. Patterson also repeatedly refused to say how much Fujitsu would contribute to the £1.8bn redress scheme for victims of the scandal, currently funded by taxpayers. More than 900 sub-postmasters were prosecuted after the faulty Horizon computer system made it look like money was missing from their branch accounts.

While much research in artificial intelligence (AI) has focused on scaling capabilities, the accelerating pace of development makes countervailing work on producing harmless, "aligned" systems increasingly urgent. Yet research on alignment has diverged along two largely parallel tracks: safety--centered on scaled intelligence, deceptive or scheming behaviors, and existential risk--and ethics--focused on present harms, the reproduction of social bias, and flaws in production pipelines. Although both communities warn of insufficient investment in alignment, they disagree on what alignment means or ought to mean. As a result, their efforts have evolved in relative isolation, shaped by distinct methodologies, institutional homes, and disciplinary genealogies. We present a large-scale, quantitative study showing the structural split between AI safety and AI ethics. Using a bibliometric and co-authorship network analysis of 6,442 papers from twelve major ML and NLP conferences (2020-2025), we find that over 80% of collaborations occur within either the safety or ethics communities, and cross-field connectivity is highly concentrated: roughly 5% of papers account for more than 85% of bridging links. Removing a small number of these brokers sharply increases segregation, indicating that cross-disciplinary exchange depends on a handful of actors rather than broad, distributed collaboration. These results show that the safety-ethics divide is not only conceptual but institutional, with implications for research agendas, policy, and venues. We argue that integrating technical safety work with normative ethics--via shared benchmarks, cross-institutional venues, and mixed-method methodologies--is essential for building AI systems that are both robust and just.

Each year millions of people seek help for their legal problems by calling a legal aid program hotline, walking into a legal aid office, or using a lawyer referral service. The first step to match them to the right help is to identify the legal problem the applicant is experiencing. Misdirection has consequences. Applicants may miss a deadline, experience physical abuse, lose housing or lose custody of children while waiting to connect to the right legal help. We introduce and evaluate the FETCH classifier for legal issue classification and describe two methods for improving accuracy: a hybrid LLM/ML ensemble classification method, and the automatic generation of follow-up questions to enrich the initial problem narrative. We employ a novel data set of 419 real-world queries to a nonprofit lawyer referral service. Ultimately, we show classification accuracy (hits@2) of 97.37\% using a mix of inexpensive models, exceeding the performance of the current state-of-the-art GPT-5 model. Our approach shows promise in significantly reducing the cost of guiding users of the legal system to the right resource for their problem while achieving high accuracy.