The framework includes proposals for child privacy protections, fewer restrictions around data center buildout and vague ideas about IP licensing. The White House has announced a new AI policy framework that calls for Congress to craft federal regulation that overrules state AI laws. The Trump administration has made multiple attempts to overrule more restrictive state-level AI regulation, but has failed so far, most notably in the passing of the "One Big Beautiful Bill." The framework focuses on a variety of topics, covering everything from child privacy to the use of AI in the workforce. "Importantly, this framework can succeed only if it is applied uniformly across the United States," The White House writes.

The GPUs were placed in servers that were supposed to be shipped from Taiwan to companies in Southeast Asia. The US Attorney's Office for the Southern District of New York has charged three people with illegally exporting NVIDIA GPUs to China in violation of the Export Control Reform Act. NVIDIA's chips have become a critical component in the rush to train and run increasingly complex artificial intelligence models, one the US has sought to manipulate with export controls and profit-sharing schemes with NVIDIA. The three people, Yih-Shyan Wally Liaw, Ruei-Tsang Steven Chang and Ting-Wei Willy Sun, two employees and one contractor working for US IT company Super Micro Computer, allegedly circumvented export control laws via a multi-step scheme that involved creating fake orders for servers with NVIDIA chips from Southeast Asian companies, that were then secretly sent to China. The plan involved paying a logistics company to repackage the servers in Taiwan, staging dummy servers to be inspected by Super Micro Computer's compliance team and falsifying records so Liaw, Chang and Sun's employer was unaware where the servers were actually being sent.

A judge ordered Kalshi to immediately halt sports and election contracts in the state, intensifying a growing regulatory battle over prediction markets. Kalshi has been temporarily banned in Nevada, marking the latest escalation in the widening regulatory war over prediction markets. The First Judicial District Court of Nevada has issued a 14-day restraining order, effective immediately, barring the company from "offering a derivatives exchange and prediction market which offers event-based contracts relating to sports, election, and entertainment related events" without first obtaining gaming licenses. This is the first time a US state has forced the company to cease operations. This particular legal battle began just over a year ago, when Nevada regulators sent Kalshi a cease-and-desist letter demanding that it stop offering sports-related events contracts.

Luke Littler, the youngest darts world champion in history, has applied to the Intellectual Property Office to trademark his face. The move is intended to prevent his face being reproduced, including by generative AI, without permission. Littler has won two World Championship titles in a row and has had his image used legally on darts merchandise, as well as by multiple brands such as KP Nuts. The 19-year-old joins celebrities such as actor Matthew McConaughey who have filed to protect their likeness from AI misuse in recent months. Littler has already trademarked his nickname the Nuke in the United States.

Tech companies believe in intellectual property, but not yours. In April 2024, Eric Schmidt, the former Google CEO and a current AI evangelist, gave a closed-door lecture to a group of Stanford students. If these young people hoped to be Silicon Valley entrepreneurs, Schmidt explained, then they should be prepared to breach some ethical boundaries. Yet Schmidt told the students to go ahead and download whatever they need to build an accurate "test" version of their AI product. If the product takes off, "then you hire a whole bunch of lawyers to go clean the mess up," he said.

Three people associated with artificial intelligence server maker Super Micro Computer, including its cofounder, have been charged with helping smuggle at least $2.5bn-worth of United States AI technology to China in violation of export laws, according to the US Department of Justice. US prosecutors did not name Super Micro in the complaint, referring only to a "US manufacturer", but San Jose, California-based Super Micro said it was informed by federal prosecutors of the indictment on Thursday. The Justice Department said it had charged Yih-Shyan Liaw, Ruei-Tsang Chang, and Ting-Wei Sun in an indictment unsealed in federal court in Manhattan on Thursday, on allegations of a complex scheme to send US-made servers through Taiwan to other countries in Southeast Asia, where they were swapped into unmarked boxes and sent on to China. The US has had export restrictions on China for advanced AI chips since 2022. In a release, FBI Assistant Director in Charge James Barnacle said the defendants used fabricated documents, staged bogus equipment to pass audit inventories, and used a pass-through company to conceal their misconduct and true clientele list.

A trio linked with a US technology supplier have been charged over a ploy to smuggle American artificial intelligence (AI) chips to China, the Department of Justice said on Thursday. The individuals allegedly conspired to sell billions of dollars' worth of technology to buyers in China by faking documents and using dummy equipment to slip past audits, according to the DOJ. The goods in question included Nvidia-made semiconductors, highly coveted AI chips which are subject to export controls. In August 2025, two Chinese nationals were also arrested and charged with illegally shipping millions of dollars' worth of Nvidia chips to China. The DOJ said in a statement on Thursday that it had arrested US-citizen Yih-Shyan Wally Liaw and Taiwanese citizen Ting-Wei Willy Sun, while Ruei-Tsang Steven Chang, a Taiwanese citizen, remains a fugitive.

Emerging ethical approaches have attempted to filter pretraining material, but such approaches have been ad hoc and failed to take context into account. We offer an approach to filtering grounded in law, which has directly addressed the tradeoffs in filtering material. First, we gather and make available the Pile of Law, a ~256GB (and growing) dataset of open-source English-language legal and administrative data, covering court opinions, contracts, administrative rules, and legislative records. Pretraining on the Pile of Law may help with legal tasks that have the promise to improve access to justice. Second, we distill the legal norms that governments have developed to constrain the inclusion of toxic or private content into actionable lessons for researchers and discuss how our dataset reflects these norms. Third, we show how the Pile of Law offers researchers the opportunity to learn such filtering rules directly from the data, providing an exciting new research direction in model-based processing.

Despite the success of reinforcement learning from human feedback (RLHF) in aligning language models, current reward modeling heavily relies on experimental feedback data collected from human annotators under controlled and costly conditions. In this work, we introduce observational reward modeling -- learning reward models with observational user feedback (e.g., clicks, copies, and upvotes) -- as a scalable and cost-effective alternative. We identify two fundamental challenges in this setting: (1) observational feedback is noisy due to annotation errors, which deviates it from true user preference; (2) observational feedback is biased by user preference, where users preferentially provide feedback on responses they feel strongly about, which creats a distribution shift between training and inference data. To address these challenges, we propose CausalRM, a causal-theoretic reward modeling framework that aims to learn unbiased reward models from observational feedback. To tackle challenge (1), CausalRM introduces a noise-aware surrogate loss term that is provably equivalent to the primal loss under noise-free conditions by explicitly modeling the annotation error generation process. To tackle challenge (2), CausalRM uses propensity scores -- the probability of a user providing feedback for a given response -- to reweight training samples, yielding a loss function that eliminates user preference bias. Extensive experiments across diverse LLM backbones and benchmark datasets validate that CausalRM effectively learns accurate reward signals from noisy and biased observational feedback and delivers substantial performance improvements on downstream RLHF tasks -- including a 49.2% gain on WildGuardMix and a 32.7% improvement on HarmBench. Code is available on our project website.

Striking an optimal balance between predictive performance and fairness continues to be a fundamental challenge in machine learning. In this work, we propose a post-processing framework that facilitates fairness-aware prediction by leveraging model ensembling. Designed to operate independently of any specific model internals, our approach is widely applicable across various learning tasks, model architectures, and fairness definitions. Through extensive experiments spanning classification, regression, and survival analysis, we demonstrate that the framework effectively enhances fairness while maintaining, or only minimally affecting, predictive accuracy.