With an eerie, robot-like appearance and an otherworldly glow when worn, those LED masks all over your FYP give off a science-fiction vibe. Fittingly, it was researchers with NASA who discovered the potential for medical light therapy to treat wounds, arthritis, glaucoma and other ailments in the 1990s. By the early 2000s, that LED light therapy was growing in popularity at dermatology offices where patients donned an LED mask or used similar devices to slow aging and treat acne. And now that technology has trickled down to our homes. Brands like Omnilux and Dr. Gross have popularized direct-to-consumer LED masks that are safe to use regularly in the privacy of your own home, available at a range of price points (from under 100 to nearly 500).

The millions that US tech companies invested in currying favor with Donald Trump seemed to pay off this week as the new administration issued a flurry of directives that relaxed regulations and dropped lawsuits previously aimed at holding the industry to account. Crypto, AI and social media companies, many of which made donations to Trump, are all expecting to benefit. At the center of the administration's moves is Elon Musk, the world's richest man. Over the past week, federal agencies under the president's authority dropped legal fights against his rocket company and the US's biggest cryptocurrency exchange. The White House also issued a "deregulatory initiative" aimed at loosening tech-sector regulation by empowering Musk's Doge.

Grindr, the dating app that caters to gay men, cannot be held responsible for the rape of a 15-year-old boy who the company matched with sexual predators, the U.S. 9th Circuit Court of Appeals ruled this week; it is the latest teens-versus-tech spat in a fight over internet immunity experts say could soon come before the U.S. Supreme Court. The appellate court's ruling upheld a 2023 decision by U.S. District Judge Otis D. Wright II of the Central District of California, who dismissed the suit, saying Grindr was shielded by broad immunity protections passed almost a decade before the plaintiff was born. In a series of events Wright called "alarming and tragic," a closeted Nova Scotia teen downloaded the LGBTQ hookup app in an attempt to meet other gay kids in his rural Canadian town. Instead, over the course of four days, he was assaulted by four adult men, including a man who picked him up after the teen sent him pictures from his high school cafeteria. LGBTQ social networking platform Grindr last year told its all-remote staff they had to return to the office or lose their jobs.

"It shouldn't take longer to approve an [energy] project than it takes to build it," said National Governors Association Vice Chair Kevin Stitt at Friday's conference in Washington. That, the Oklahoma Republican said, is the collective picture painted of all the problems with government bureaucracy at all levels that imperils the U.S.' ability to stay ahead of China in terms of cyberthreat-prevention and energy dominance. Permitting reform is one of the most important things to address with a new administration and new state government sessions beginning, the governors collectively expressed. There was bipartisan consensus at the NGA that America must move responsibly toward a future secure from malign foreign actors in both cybersecurity and energy development. "Permitting reform is one of those issues where both Republicans and Democrats recognize the problem, we largely agree on solutions," Stitt said, adding it is a national security issue that the U.S. must streamline permitting.

Retrieval-augmented generation (RAG) has shown impressive capabilities in mitigating hallucinations in large language models (LLMs). However, LLMs struggle to handle misleading retrievals and often fail to maintain their own reasoning when exposed to conflicting or selectively-framed evidence, making them vulnerable to real-world misinformation. In such real-world retrieval scenarios, misleading and conflicting information is rampant, particularly in the political domain, where evidence is often selectively framed, incomplete, or polarized. However, existing RAG benchmarks largely assume a clean retrieval setting, where models succeed by accurately retrieving and generating answers from gold-standard documents. This assumption fails to align with real-world conditions, leading to an overestimation of RAG system performance. To bridge this gap, we introduce RAGuard, a fact-checking dataset designed to evaluate the robustness of RAG systems against misleading retrievals. Unlike prior benchmarks that rely on synthetic noise, our dataset constructs its retrieval corpus from Reddit discussions, capturing naturally occurring misinformation. It categorizes retrieved evidence into three types: supporting, misleading, and irrelevant, providing a realistic and challenging testbed for assessing how well RAG systems navigate different retrieval information. Our benchmark experiments reveal that when exposed to misleading retrievals, all tested LLM-powered RAG systems perform worse than their zero-shot baselines (i.e., no retrieval at all), highlighting their susceptibility to noisy environments. To the best of our knowledge, RAGuard is the first benchmark to systematically assess RAG robustness against misleading evidence. We expect this benchmark will drive future research toward improving RAG systems beyond idealized datasets, making them more reliable for real-world applications.

The current discourse on large language models (LLMs) and copyright largely takes a "behavioral" perspective, focusing on model outputs and evaluating whether they are substantially similar to training data. However, substantial similarity is difficult to define algorithmically and a narrow focus on model outputs is insufficient to address all copyright risks. In this interdisciplinary work, we take a complementary "structural" perspective and shift our focus to how LLMs are trained. We operationalize a notion of "fair learning" by measuring whether any training decision substantially affected the model's memorization. As a case study, we deconstruct Pythia, an open-source LLM, and demonstrate the use of causal and correlational analyses to make factual determinations about Pythia's training decisions. By proposing a legal standard for fair learning and connecting memorization analyses to this standard, we identify how judges may advance the goals of copyright law through adjudication. Finally, we discuss how a fair learning standard might evolve to enhance its clarity by becoming more rule-like and incorporating external technical guidelines.

The EU AI Act introduces a blanket protection of fundamental rights for specific applications of AI that it classifies as high-risk, which is implemented under the existing single market harmonised product certification mechanisms for health and safety protection, i.e. the New Legislative Framework. This protection of fundamental rights places many AI issues previously covered by voluntary trustworthy or ethical AI frameworks into a framework with independent and legally binding accountability for harmful characteristics of products grounded in the same human rights framework underpinning Union Law and many national laws. However, this major change in accountability also introduces many legal uncertainties on how AI providers and deployers can identify and manage risks to fundamental rights. Contrast this to the introduction of GDPR, which focussed on the protection of rights of privacy and data protection but benefitted from the development and employment of data protection principles under the data protection directive which had been in force beforehand. The protection of fundamental rights in AI systems however benefits from no such breakdown of principle, nor from prior deployment or compliance experience with such principles. This presents an extremely high level of legal uncertainty for providers and deployers of AI systems once the Act comes into force. The associated burden or chilling effects may fall disproportionately on public bodies wishing to deploy and reap the benefits of AI in high risk areas, and indigenous companies and especially SMEs that wish to market products into such applications.

It has been 100 years since statistical process control (SPC) or statistical process monitoring (SPM) was first introduced for production processes and later applied to service, healthcare, and other industries. The techniques applied to SPM applications are mostly statistically oriented. Recent advances in Artificial Intelligence (AI) have reinvigorated the imagination of adopting AI for SPM applications. This manuscript begins with a concise review of the historical development of the statistically based SPM methods. Next, this manuscript explores AI and Machine Learning (ML) algorithms and methods applied in various SPM applications, addressing quality characteristics of univariate, multivariate, profile, and image. These AI methods can be classified into the following categories: classification, pattern recognition, time series applications, and generative AI. Specifically, different kinds of neural networks, such as artificial neural networks (ANN), convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial networks (GAN), are among the most implemented AI methods impacting SPM. Finally, this manuscript outlines a couple of future directions that harness the potential of the Large Multimodal Model (LMM) for advancing SPM research and applications in complex systems. The ultimate objective is to transform statistical process monitoring (SPM) into smart process control (SMPC), where corrective actions are autonomously implemented to either prevent quality issues or restore process performance.

This work is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). For the definitive version, see 10.1109/ACCESS.2025.3539933. Disclaimer: This research involves topics that may include disturbing results. Any explicit content has been redacted, and potentially disturbing results have been presented in a neutral and anonymized manner to minimize emotional distress to the readers. Abstract --Novel research aimed at text-to-image (T2I) generative AI safety often relies on publicly available datasets for training and evaluation, making the quality and composition of these datasets crucial. This paper presents a comprehensive review of the key datasets used in the T2I research, detailing their collection methods, compositions, semantic and syntactic diversity of prompts and the quality, coverage, and distribution of harm types in the datasets. By highlighting the strengths and limitations of the datasets, this study enables researchers to find the most ...

Conversational agents are increasingly woven into individuals' personal lives, yet users often underestimate the privacy risks involved. The moment users share information with these agents (e.g., LLMs), their private information becomes vulnerable to exposure. In this paper, we characterize the notion of contextual privacy for user interactions with LLMs. It aims to minimize privacy risks by ensuring that users (sender) disclose only information that is both relevant and necessary for achieving their intended goals when interacting with LLMs (untrusted receivers). Through a formative design user study, we observe how even "privacy-conscious" users inadvertently reveal sensitive information through indirect disclosures. Based on insights from this study, we propose a locally-deployable framework that operates between users and LLMs, and identifies and reformulates out-of-context information in user prompts. Our evaluation using examples from ShareGPT shows that lightweight models can effectively implement this framework, achieving strong gains in contextual privacy while preserving the user's intended interaction goals through different approaches to classify information relevant to the intended goals.