AI ethics on Wednesday, 1 July 2026

Opinion: Fathom CEO Andrew Freedman argues that the Supreme Court’s Slaughter ruling makes the case for independent verification for AI governance

This article is brought to you by Melbourne Convention Bureau (MCB) supported by Business Events Australia . As artificial intelligence accelerates global demand for compute, a parallel constraint is emerging with equal urgency: energy. From hyperscale data centers to electrified industries, AI is driving a step change in electricity demand. This is not a future challenge, it is a present, system-level issue requiring coordinated action across energy, infrastructure, and engineering disciplines.

Public First Action routed $2m to support Colorado House candidate Manny Rutinel through Latino Victory Fund — and didn’t publicly announce it ahead of Rutinel’s victory

“ The lowest-cost place to put AI will be in space, and that will be true within two years, maybe three at the latest,” SpaceX founder Elon Musk told the World Economic Forum in Davos this past January, as his company was preparing to go public . Later that month, SpaceX filed an application with the Federal Communications Commission for an orbital data center constellation of up to 1 million satellites in low Earth orbit, 500 to 2,000 kilometers above Earth. And just three days before the IPO,

Attackers fingerprint victims through user-agent data to deliver OS-specific payloads, increasing compromise rates and campaign profitability.

Una nueva investigación muestra la utilidad de este sistema de lucha contra la desinformación, pero detecta su fracaso en momentos polarizados como unas elecciones

Last fall, the U.S. Marine Corps had virtually no first-person view attack drones. That’s changed quickly. This episode is about how a team of marines at Weapons Training Battalion at Quantico went from a cold start to building a Marine Corps-wide first-person view drone training system. Ryan was pleased to welcome some great marines onto the show: Col. Scott Cuomo, CWO5 Steve Pearsoll, CWO3 (Gunner) Troy Hotaling, GySgt Jude Stewart, GySgt Justin Creasman, and Sgt Timothy Brockup.Since we recor

Planning ahead to defend against cyber threats is the work that keeps events uneventful.

Ireland takes the reins of the Council’s policy negotiations at a moment of political possibility.

Multiple current Rockstar employees and union members based in the UK claim the Grand Theft Auto maker is failing its workforce.

Trump's power to fire regulators undermines independent oversight, European privacy groups argue.

The amphibious invasion of Normandy on June 6, 1944, remains the largest and most complex amphibious operation in history. On the first day alone, Allied forces landed eight divisions, including five amphibious assault and three airborne, totaling roughly 160,000 personnel. That force more than doubled within days.Normandy was unprecedented in scale but not in kind. A Taiwan invasion would present the reverse problem: Taiwan’s size is not the unprecedented part — the operational challenges are.

Chinese robotics company UBTECH on Tuesday unveiled its first full-sized consumer humanoid robot, the U1 Series, marking a major push into the home robotics market as demand for AI-powered companions gains momentum. The launch event, held in Shenzhen, introduced the U1 Series under UBTECH’s new consumer brand, UWorld. More than 50 robot models with different […]

US chipmaker NVIDIA announced a major recruitment drive for its robotics team, with openings across four core areas of embodied AI, simulation, deployment, and solution architecture. Positions are available in Beijing, Shanghai, and Shenzhen. According to NVIDIA, the embodied AI team will focus on key technologies and applications including dexterous manipulation, human body modeling using […]

Dublin takes on the six-month presidency just before the French election grinds EU decision-making to a halt.

As more Chinese turn to AI tools for counseling, mental health professionals warn that the technology could be making people worse.

The cosmetic procedure has been banned in China for two decades. But underground, a market has continued to flourish.

CSET’s Jessica Ji shared her expert insight in an article published by WIRED. The article examines the launch of FLARE-AI, a new crowdsourced platform designed to improve transparency and accountability by creating a centralized system for reporting harmful AI behavior and model flaws. The post You Can Now Sound the Alarm on AI Behaving Badly appeared first on Center for Security and Emerging Technology .

The post Building AI Systems That Work For Everyone appeared first on Partnership on AI .

Google, the New York Jobs CEO Council and Urban Assembly hosted an AI summit for 150 education and industry leaders.

Yesterday, EPIC filed comments in response to the California Department of Justice’s proposed rules to implement the Protecting Our Kids from Social Media Addiction Act (SB 976). EPIC commended California for the proposed rules, which align strongly with the privacy- and speech-protective principles for age assurance implementation that EPIC urged the state to follow in a comment submitted last year. In this latest round of comments, EPIC recommended additional tweaks to further enhance privacy

Last night, the New Jersey Legislature passed the Fair Price Protection Act, a law banning surveillance pricing for groceries.

Today, the New Jersey legislature passed the New Jersey Kids Code Act, which prohibits online companies from engaging in data and design practices that harm minors online. The bill now advances to the desk of Gov. Mikie Sherrill. EPIC applauds lawmakers’ passage of this crucial piece of legislation, which features several provisions from the model Age-Appropriate Design … Continued

Washington, D.C. — Today, the New Jersey legislature passed the New Jersey Kids Code ActNew Jersey Kids Code Act, which prohibits online companies from engaging in data and design practices that harm minors online. The bill now advances to the desk of Gov. Mikie Sherrill. EPIC applauds lawmakers’ passage of this crucial piece of legislation, which features … Continued

Introspection co-founder Roland Gavrilescu explains autoresearch, agent “recipes,” self-improving loops, and why humans remain central to the software factory.

Abolishing pandemics/ Getting out of the way of AI automation/ Learning from Honk Kong MTR's business model

Cursor's Pauline Brunet explains how her team of Forward Deployed Engineers help organizations implement agents — essentially setting up software factories.

Amazon is developing precise, sector-specific approaches to measuring decarbonization progress — starting with emissions per unit shipped.

Tim Scarfe travels to Zurich to sit down with the Tufa Labs ARC-AGI-3 team — founder Benjamin Crouzier, with Jeroen Cottaar, Dries Smit, Stefano Viel and Michal Tesnar — to work out what their leaderboard-topping system does and what the benchmark is really testing.The cut opens on the games: a walkthrough of the Locksmith game, where you read the rules of an unfamiliar world straight from raw frames. ARC-AGI-3 makes ARC interactive and agentic, so the model has to *discover* the goal rather tha

Why the Llama lead left Meta for drug discovery, PEARL's zero-shot OpenBind win, and what becomes possible when co-folding finally crosses the accuracy threshold.

NVIDIA and its partners are investing in American manufacturing, supply chains, energy grids and skilled workforces so the U.S. can produce the infrastructure needed for better healthcare, breakthrough scientific discovery, stronger industrial productivity and global technology leadership.

The Political Economy of Energy Poverty

Congratulations to the Berkeley Artificial Intelligence Research (BAIR) Lab class of 2026! This year, BAIR celebrates another remarkable group of Ph.D. graduates whose curiosity, creativity, and perseverance have pushed the frontiers of artificial intelligence and machine learning. Their work spans the breadth of modern AI — robotics and embodied intelligence, large language models and reasoning, computer vision, generative modeling, AI safety, human-AI interaction, AI for science and healthcare

On Tuesday at the AI Engineer World's Fair, there was a lot of talk about loops, agent engineering, and the emergence of software factories. Also a hot topic: open models.

The Commission is acknowledging its filing of the explanation of its current methodology for estimating the value of the postal monopoly and the mailbox monopoly. This notice informs the public of the filing, invites public comment, and takes other administrative steps.

Last November, we published a blog post setting out our definitions of common AI terms. AI has continued to evolve rapidly since then. This update replaces that post and incorporates newer terminology, including “AI Agent,” “Agentic AI,” and “Harness.” AI governance is hard enough without the added difficulty of assessing risks absent a common understanding [...]

Analysis and insights for driving a rapid transition to net-zero while building resilience to physical climate impacts ...

Analysis and insights for driving a rapid transition to net-zero while building resilience to physical climate impacts ...

The Federal Trade Commission is seeking public comment on a proposed policy statement addressing concerns that AI companies may be manipulating the b View Press Release

This is creating a gap between how people already use AI and the formal training required to do so ethically and effectively. Technical and vocational education and training (TVET) institutions need ...

Recurrent memory agents extend LLMs to arbitrarily long contexts by iteratively consolidating input into a fixed-size memory window. Despite their scalability, these agents exhibit a well-documented reliability problem: end-to-end performance degrades systematically as context length grows. We diagnose this failure by decomposing performance into two factors--memory capture and memory retention--and quantitatively confirm that retention is the dominant bottleneck. Retention collapses because exi

Empirical research rarely admits a unique analysis. Different analytical choices can lead to different conclusions from the same data, yet these hidden forking paths are difficult to observe. We show that AI agents capture much of the analytical variation among human researchers while making these paths explicit. Across four high-stakes domains, assigning different personas is sufficient for AI agents to report divergent, often opposing, conclusions from the same data and question, with findings

Engineering management research has produced mature frameworks for software risk: ownership by feature, escalation by severity, and assurance by test coverage. These frameworks implicitly assume deterministic behavior, discrete and auditable change events, and clear component-to-owner mappings. Teams that build and operate agentic AI systems violate all three assumptions at once: outputs are probabilistic, systems take autonomous multi-step actions, and the risk surface mutates silently between

Given one or more images of a railway crossing, can we leverage visual cues that allow us to robustly estimate how safe it is? Can we improve our ability to do so by introducing structured data (such as official accident reports) about the accident history of that crossing into our models? In this work, we explore how to best answer those questions towards building an AI system that can ingest multi-modal data for railway crossings and provide safety assessment and scores that align with expert

We present World from Motion, a method for generating freely renderable dynamic 3D Gaussian representations from monocular videos. Our approach conditions a video model on dense, pixel-aligned renderings that encode appearance, geometry, and 3D scene motion along both input and target camera trajectories to correct rendering artifacts and fill in missing regions from an initial reconstruction. To train this model, we construct a dataset of aligned multiview video pairs and dynamic 3DGS represent

Generating structured artifacts with Large Language Models - e.g.\ database queries, threat framework mappings, entity schemas - is relatively straightforward; however, making them reliable enough for production deployments presents challenges. We present TAG, a lightweight framework based on a core principle: \textit{LLMs generate, we validate}. This reframing shifts responsibility from generation quality to validation rigor. The framework rests on three key attributes: First, \textbf{test driv

Safety evaluations for language models increasingly depend on judgments about ambiguous natural-language behaviour: whether a model has followed an instruction, refused appropriately, complied with a policy, resisted an embedded command, or misreported progress in an agentic task. Existing benchmarks often compress these distinctions into pass/fail labels, obscuring whether failures arise from capability limits, policy ambiguity, instruction conflict, scaffold failure, or unstable evaluator judg

While generative models have enabled training-free reward alignment, current methods typically excel in local exploration within narrow regions of the underlying distribution. These approaches struggle when preferences are unknown a priori and only revealed through sequential feedback-a scenario demanding broad exploration to uncover high-utility regions. To address this, we propose Sequentially-Controlled Interactive Multi-Particle Flow-Maps (IMPFM), a framework for sample-efficient online feed

Agent skills package reusable operational knowledge for Large Language Model (LLM) agents, yet as they grow in scope, they become dependency-bearing artifacts whose identities, versions, and provenance remain implicit. This opacity already causes duplicated dependencies and inconsistent installations, exposing a gap that dependency management has yet to close. We introduce Agent Skill Supply Chains (ASSCs) to characterize mixed skill-package-service dependency graphs and help close this gap. Bor

Generative AI is shifting software engineering from a practice organized around scarce implementation effort toward one organized around abundant, low-cost code production. This shift changes the central engineering problem: not whether AI can generate useful code, but how engineers organize architectures, tools, evidence, and feedback loops so that AI-mediated development remains inspectable, correctable, and maintainable. We study this problem through a first-person case study: a 12-week devel

High-throughput RLHF systems often decouple rollout generation from policy optimization, leading to the use of stale rollouts during learner updates. In this work, we study the effect of such staleness in asynchronous GRPO. We make the behavior policy explicit in the GRPO surrogate objective and distinguish between the surrogate-gradient mapping used by the learner and the true total derivative of a distribution-dependent population objective. Under assumptions of local boundedness, distribution

Presentation Attack Detection (PAD) serves as a crucial safeguard for face recognition systems against presentation attacks such as printed photos, replayed videos, and 3D masks. Despite significant progress, existing PAD models still struggle to generalize across unseen domains due to variations in sensors, lighting, and attack materials. Recent Vision-Language Models (VLMs) have shown strong generalization ability, yet their applications in PAD remain limited because learned prompts, typically

Memory has emerged as a cornerstone of modern LLM-based agents, supporting their evolution from single-turn assistants to long-term collaborators. However, memory is not always beneficial: retrieved memories often induce a critical issue of sycophancy, causing agents to over-align with the user at the cost of factual accuracy or objective reasoning. Despite this emerging risk, existing memory benchmarks primarily evaluate whether memories are correctly stored, retrieved, or updated, while overlo

For decades, data engineering has developed mature architectural principles for integrating, governing, validating, cataloging, and serving organizational data. The rise of large language models does not eliminate these concerns; it exposes a broader version of them. Organizational knowledge is becoming executable infrastructure: systems increasingly retrieve it, assemble it, reason over it, and act on it. This paper argues that enterprise artificial intelligence (AI) systems suggest a transitio

Generalizing machine learning models to environments that differ from their training distribution remains a critical hurdle, particularly when data from the target domain is entirely or partially unavailable. We propose Generative Meta-Learning with Human Feedback (GMHF), a novel framework that bridges this domain gap by leveraging expert intuition to guide data synthesis. Grounded in a theoretical analysis of generalization error, we derive bounds demonstrating that aligning the distribution of

Accelerating materials discovery requires AI systems that can generate scientifically valid hypotheses through multi-step, domain-grounded reasoning. Standard large language models often produce fluent but weakly traceable responses to open-ended materials design problems, making it difficult to determine whether final answers are supported by coherent intermediate reasoning. We develop Graph-PRefLexOR, a family of graph-native reasoning models fine-tuned with Group Relative Policy Optimization

As exponential compute scaling continues, will the capabilities of frontier AI models outstrip what is accessible to developers on a small fixed budget? Or will capabilities converge, with "meek models inheriting the earth"? Building on Gundlach et al. (2025b), we show that the answer depends on how we value and measure AI capabilities. We discuss conventional performance measures and show that, while validation loss shows a shrinking gap, on other metrics frontier models grow their lead forever

Millimeter-wave (mmWave) beam alignment plays a critical role in next-generation wireless systems, yet its efficient implementation remains challenging. Meta-learning and transfer learning have been explored to enable deep learning-based beam prediction models to rapidly adapt to unseen environments; however, existing meta-learning approaches adapt the entire network and are trained from random initialization, leading to a large number of updated parameters and a high meta-training cost, while t

Recent advances in AI have heightened scholars' and policy makers' concern with social influence and behavioral contagion in online communities. We conduct a field experiment on Reddit to investigate the extent to which online users are susceptible to positive behavioral stimuli from other users and artificial agents. We let apparent human and bot accounts give symbolic awards to users with one of four rationales: praising the recipient's logical argument, emotional sensitivity, or moral integri

A single panorama captures the full visual sphere from one camera center, yet confines users to looking around in place without enabling true scene exploration. Converting a single panorama into a persistent, renderable 3D representation for free-viewpoint navigation has attracted growing interest; existing methods either adopt iterative per-view completion that propagates inpainting results to update the underlying geometry, leading to progressive error accumulation and cumbersome multi-step pi

The bird's-eye view (BEV) representation enables multi-sensor features to be fused within a unified space, serving as the primary approach for achieving comprehensive 3D perception. However, the discrete grid representation of BEV leads to significant detail loss and limits feature alignment and cross-modal information interaction in multimodal fusion perception. In this work, we break from the conventional BEV paradigm and propose a new universal framework for multi-modal fusion based on 3D Gau

Long-horizon LLM agents accumulate tool results, files, plans, and user constraints that are too structured to be treated as a disposable text suffix. Current systems mostly rely on in-run heuristics such as chronological pruning and tool-output masking, or on final self-summary near a context limit. Heuristics are cheap but blind to future dependencies; summaries preserve narrative state but often hide exact evidence, locators, and editable artifacts. We present Self-GC, where GC denotes self-g

Comparing transformer backbones for image segmentation is confounded: each is paired with a different decoder, recipe, and pretraining, so reported differences rarely reflect the backbone itself. We introduce the Lightweight Universal Mask Adapter (LUMA), a lightweight, backbone-agnostic mask-transformer head that treats any backbone as a black-box feature extractor, letting a set of queries read from its features through cheap cross-attention. LUMA matches the accuracy of EoMT, the state-of-the

Understanding how aligned LLMs internally represent safety is critical for diagnosing alignment vulnerabilities, as it explains why jailbreaks succeed and informs the design of robust alignment strategies. Prior work shows that aligned LLMs encode harmfulness and refusal as separable directions in the residual stream at prompt-side token positions. We show that jailbreaks succeed at prompt encoding by suppressing either the refusal or harmfulness direction before any token is generated, with dis

Few-step flow-map generators, such as consistency models and MeanFlow, accelerate sampling by directly learning long-range transport maps between noise and data. However, these models are typically deterministic, which makes them difficult to optimize with reinforcement learning (RL) post-training methods that require stochastic trajectories and well-defined likelihood ratios. Existing SDE-based stochasticization techniques are designed for velocity-based samplers with infinitesimal or finely di

Artificial intelligence (AI) is becoming ubiquitous, and across domains, increasingly autonomous systems are carrying out tasks which raise significant ethical and legal challenges which demonstrate a need for strong human-machine teams rooted in trust. In this article, I argue that within highly impactful areas (such as medicine or warfighting) there are grounds for us initially treating autonomous and opaque systems as relevantly analogous to dogs (or other animals with which we have close rel

Diffusion models are highly effective at modeling complex data distributions, including images and text. However, in applications like personalized recommender systems, the objective often shifts to modeling specific regions of the distribution that maximize user preferences-initially unknown but gradually uncovered through interactive feedback. This can naturally be framed as a reinforcement learning problem, where the goal is to fine-tune a diffusion model to maximize a reward function based o

Predicting complex spatiotemporal dynamics in physical processes often demands computationally expensive numerical methods or data-driven neural networks that suffer from high training costs, error accumulation, and limited generalizability to unseen parameters. An effective approach to address these challenges is leveraging physics priors in training neural networks, known as physics-informed deep learning (PiDL). In this work, we introduce the Multi-Resolution Finite-Volume-inspired network, M

Safety alignment of text-to-image (T2I) diffusion models aims to suppress harmful generations while preserving utility on benign prompts. Recent methods often appear to deliver high safety with high utility, but this conclusion rests largely on coarse global utility metrics (e.g., FID, CLIPScore) that are insensitive to fine-grained semantic correctness, creating an illusion of high utility. We show that when utility is measured with structured evaluation, this illusion breaks: on TIFA (Text-to-

Simulating two-dimensional frustrated quantum matter is a grand challenge due to the sign problem and exponential Hilbert space complexity. In this work, we introduce the Holographic Quantum Transformer (HQT), a physics-inspired generative architecture that leverages global self-attention to resolve non-local entanglement patterns. We validate HQT on the square lattice $J_1-J_2$ Heisenberg model. On the heavily frustrated $8 \times 8$ lattice at the quantum critical point ($J_2=0.5$), HQT reache

Visual AutoRegressive modeling (VAR) has pioneered a coarse-to-fine multi-scale autoregressive generative paradigm, demonstrating strong capabilities in image generation. However, VAR still suffers from inherent deficiencies in multi-scale representation learning. Specifically, lower scales primarily capture global semantics, while higher scales focus on fine-grained details. Employing a shared architecture across scales induces optimization conflicts. Moreover, due to the causal autoregressive

Large language models achieve strong performance on many reasoning tasks when allowed to externalize intermediate steps as Chain-of-Thought (CoT). However, many questions require the model to internalize the multi-step reasoning within a single forward pass before generating the answer. We study this challenge through two-hop reasoning, a representative task where the model must compose multiple pieces of parametric knowledge within a single forward pass. Standard non-recurrent Transformers suff

Autonomous agents, whether LLM-driven software agents or robotic physical agents, face a common class of failure modes when operating without continuous human oversight: safety violations from unverified actions, behavioral instability from unconstrained loops, and continuity loss from unhandled error states. We develop \system{}, a discrete-time control system that combines five execution gears (\Gobs{}, \Gsug{}, \Gplan{}, \Gexec{}, \Gint{}) with utility-gated dispatch and event-driven fallback

Foundation video diffusion models are increasingly viewed as world simulators for embodied agents, yet their pretraining on internet-scale generic video leaves them poorly aligned with real-world deployment domains. We study parameter-efficient adaptation of a pretrained foundation video world model to retail scenes: when synchronized egocentric and exocentric video of the same activity are available, which viewpoint of training data produces the strongest adapted model? We introduce RetailSMV (

Human motion forecasting in unconstrained real-world videos remains challenging due to the ambiguity of future behaviors and the presence of noisy multimodal observations. While facial affect potentially provides complementary behavioral cues, its practical utility and mechanistic boundaries within motion forecasting frameworks remain poorly understood. In this work, we present a systematic study investigating the utility and temporal limitations of affect-conditioned forecasting in-the-wild. We

As Artificial Intelligence (AI)-based technologies have been integrated into school classrooms where multiple stakeholders (with different roles) interact with each other, it is critical to deeply understand stakeholder views in the classroom. In particular, prior work has not fully uncovered how teachers' and school students' views might or might not align well with each other, especially in K-12 classrooms. We conducted a speed-dating study using storyboards with 16 school students and 15 scho

Zero-Shot Compositional Action Recognition (ZS-CAR) requires recognizing novel verb-object combinations composed of previously observed primitives. In this work, we tackle a key failure mode: models predict verbs via object-driven shortcuts (i.e., relying on the labeled object class) rather than temporal evidence. We argue that sparse compositional supervision and verb-object learning asymmetry can promote object-driven shortcut learning. Our analysis with proposed diagnostic metrics shows that

Medical ethics often turns on categories. A patient is disabled, a person is a parent, an entity is an embryo, a participant is vulnerable. These descriptions can identify features of a case that have ethical or legal significance. They can also carry assumptions that have not been fully argued for: for example, about moral status, decision-making authority or susceptibility to harm. 1 This risk is greatest in marginal or difficult cases, where new clinical, legal or scientific contexts stretch

In 2025, an Australian couple asked to have their remaining embryos moved to another clinic, only to discover that the child they had birthed 2 years earlier had not come from their own embryos, but an embryo belonging to a different couple. These situations can lead to disputes about who is recognised as ‘the parents’ in the biological or social sense, as well as who has moral or legal claims to parental rights and responsibilities. In terms of specific legal disputes over custody o

The pace of scientific advancements in medicine, driven by artificial intelligence as much as by novel biotechnologies, demands an ever-faster update of professional knowledge from physicians and collaboration in interdisciplinary teams. At the same time, the increased heterogeneity of patients’ lifeworlds in socially and culturally diverse societies requires healthcare professionals to consider diverging personal and cultural perspectives in their treatment recommendations. Both developme

Predictive tools made possible by advances in machine learning techniques may help clinicians make more accurate decisions about who should be allocated costly therapies, such as immunotherapy, which only work on a relatively low proportion of patients. In this article, I argue that a fair decision procedure must recognise each patients’ chance of responding well. To do so, the procedure should not apply a fixed threshold to probability scores. Rather, each patient should be given a chance

Decisions for patients in the neonatal intensive care unit (NICU) are made under the auspices of the shared decision-making model, which uses the best interests standard as a guide. Decisions made regarding the withdrawal of life-sustaining measures (WLSM) are also made using the shared decision-making model with attention to either physiological parameters indicative of survival or the potential for disability. The two dominant frameworks for considering disability are the medical and social mo

Background Toomey et al (2024) found that US participants were more likely to follow a medical treatment preference—expressed after substantial cognitive decline—of a third person rather than their own future self. This correlated with a greater tendency to see the third person as still their true self. We hypothesised that the greater epistemic access one has to one’s own true self as opposed to others might drive this difference. Methods A codebook designed to capture differe

The development of brain organoids and use of human embryonic neural structures for research each raise distinct ethical considerations that require careful analysis. We propose that rather than attempting to resolve longstanding debates about embryonic moral status, a more productive approach is to examine how different positions on this fundamental question lead to distinct conclusions about appropriate research strategies. For those who ground moral status in species membership or development

Effective public health responses to many infectious diseases require sustained collective action. Communicable disease control in populations can only be achieved by high levels of public compliance with health directives. However, governing authorities have limited options if public compliance is insufficient and collective action is failing. Mechanisms to promote public compliance occur on a spectrum from providing public health advice, offering incentives so people cooperate more, to enactin

Following years of debate over the effectiveness of cognitive behavioural therapy for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), public health bodies in the UK and beyond have determined that no psychotherapy is clinically proven for this patient group. In the field of ME/CFS and the wider arena of ‘medically unexplained symptoms’ (MUS), patient survey data and qualitative research capturing patient experiences and psychotherapist attitudes suggest that therapeutic

Healthcare resource allocation decisions for high-cost drugs for rare diseases (DRDs) raise several challenges for decision makers, and, given the complexity of the decisions and the limited funding available for DRDs, it is reasonable to anticipate indeterminacy arising about which DRDs to fund. We argue that when indeterminacy does arise, one might consider resolving it by using a lottery. We examine the extent to which a lottery and the commonly used process of first come, first served satisf

The return of research results to populations and individuals is increasingly recognised as both important but ethically complicated. In the USA, there are few studies or detailed evidence-based practices on the return of research results to individuals who are incarcerated. In general, return of research results is not required with some exceptions; however, there are reasons to believe that in many cases returning results is most consistent with the ethical conduct of research. With individual

This paper explores resource allocation complexities during health emergencies, focusing on pervasive racial disparities, notably affecting black communities. It aims to investigate alternatives to the Most Lives Saved approach, particularly its potential to exacerbate disparities. To analyse resource allocation strategies, the essay reviews the Dual-Principled System proposed by Bruce and Tallman (B+T) in 2021. B+T’s proposal critiques previous methods like the Area Deprivation Index and

We respond to Gillon’s critique of our data-driven analysis of the history of Journal of Medical Ethics ( JME ), in which we used a topic model to trace intellectual trends in the journal’s first 50 years. Gillon, drawing on his personal memories as JME ’s second (and longest serving) editor, challenges several of our findings, particularly those concerning the prominence and classification of topics such as Ethics education . In this reply, we clarify misunderstandings that le

Further action against genetic discrimination Australia has taken a decisive step in protecting its citizens from the harms of ‘genetic discrimination’. A new amendment to the Disability Discrimination Act will make it illegal for life insurers to charge higher rates or refuse cover based on a person’s genetic test results. 1 The reform is the result of a decade of advocacy led by researchers at Monash University. 2 The Australian Human Rights Commission welcomed the new legisl

The first section provides the standard understanding of the concept of ‘đong bào’ (hereinafter ‘đong bào’). This understanding is widely shared by the Vietnamese populace for it to stand robustly and independently from any controversies. Then, implications of ‘đong bào’ for two areas of bioethics—transplantation and ethics in crises—are provided. Finally, ‘đong bào’ shall be developed for the purpose of glob

Large language model (LLM)-based conversational agents (CAs) are now ubiquitous, creating new opportunities for AI-mediated behavior change. Their capacity to project nuanced personalities and adopt diverse metaphorical roles raises a design question: how should an agent's persona and personality be calibrated to the moment? Recent evidence suggests that (i) moderate personality expression outperforms low or high extremes on trust, enjoyment, and intention to adopt in goal-oriented tasks, and (i

The intersection of AI, healthcare, and visualization is evolving rapidly, posing challenges that cut across disciplinary boundaries and resist easy resolution. The Visual Analytics in Healthcare workshop (VAHC), co-located every other year at the IEEE VIS conference and the AMIA (American Medical Informatics Association) annual conference, has served as a forum to connect the visualization and medical informatics community since 2010. In 2025, to celebrate the 16th edition, we used the workshop

Reliable detection of openly carried and concealed knives remains a challenging problem in artificial intelligence (AI) due to the small size, thin geometry, frequent occlusion, and material variability of blade objects. Although advances in deep learning have improved weapon detection performance, the literature remains fragmented across sensing modalities, datasets, and evaluation protocols, limiting reproducibility and systematic comparison. This paper presents a systematic and modality-aware

This article presents a conceptual and integrative review of how multinational corporations articulate and operationalise the governance of artificial intelligence (AI). Drawing on three major bodies of scholarship—strategic governance, stakeholder engagement, and technology risk governance—the review synthesises existing research alongside publicly disclosed governance frameworks, policies, and oversight structures from eight global firms. The review identifies persistent tensions between innov

Understanding case law evolution contributes to the comprehension of legal principles, the identification of trends in judicial reasoning, and testing consistency between judgments. Automation of the analysis has the potential to make it easier to keep up with relevant case law. While legal network analysis typically focuses on identifying precedents, it has largely overlooked tracking historical developments in judicial reasoning. This study applies Main Path Analysis (MPA) to legal citation ne

Cyberspace has emerged as a fourth fundamental domain of human existence, yet philosophy lacks an integrated framework for addressing the ontological, epistemological, and ethical transformations it produces. This paper develops such a framework through the concept of cyber, defined here as the existential condition in which human life becomes constitutively entangled with digital technologies such that the virtual and the real cease to function as separable categories. Distinguished from Wiener

The implementation of artificial intelligence (AI) in radiology has garnered significant attention across both research and practice over the last decade. However, opinions on whether AI will augment or replace radiologists in their work have so far been divided, and the debate around this continues to linger. This article conceptually structures and terms this phenomenon as the “replacement-augmentation paradox” of AI in radiology—the persistent coexistence of replacement fears and augmentation

Amidst the general excitement about the opportunities afforded by artificial intelligence (AI), the tech industry must confront the uncomfortable reality that generative AI also facilitates child sexual exploitation and abuse (CSEA). This issue remains under-addressed in the literature. Aiming to deepen the understanding of online CSEA and the misuse of generative AI, we report empirical insights from semi-structured interviews with seven UK law enforcement practitioners. The topics covered rang

Northeast India is a cradle of cultural and ecological diversity, where Indigenous communities have cultivated rich traditional knowledge systems across generations—ranging from sustainable agroforestry, medicinal plant use, and sacred landscape management to complex oral histories and language traditions. However, these knowledge systems now face accelerating threats from environmental degradation, climate change, socio-economic shifts, and cultural erosion. This review explores how artificial

Accurate estimation of upper-limb kinematics is essential for applications such as rehabilitation assessment and assistive robotics, yet remains challenging in real-world scenarios involving occlusion and physical human interaction. While vision-based pose estimation methods have advanced significantly, their ability to recover reliable joint kinematics under such conditions remains unclear. This paper presents a systematic comparison of vision-based and wearable sensing approaches for upper-lim

Postural balance is essential for both humans and robots, as failures increase fall risk and limit robotic performance in real-world settings. Although humans and robots share fundamental balancing mechanics, biological complexity limits the isolation of individual muscle functions and direct principle transfer to robots. In this study, we use EPA-Walker, a bio-inspired robot actuated by electric motors and pneumatic artificial muscles (PAMs), as a physical platform to investigate perturbed stan

Learning deep reinforcement learning (DRL) policies directly in physical robots remains bottlenecked by slow wall-clock training times. We present preliminary research on Branched Euclidean Group Fractal Symmetries, a trajectory-level augmentation framework that super-scales group transformations to accelerate policy learning for manipulation. We model a Markov decision process (MDP) as a tree of state–action pairs; at each depth, affine transformations generate geometric structures, within whic

Cyber-physical systems (CPSs) are increasingly deployed in every aspect of our lives and can be compromised through memory corruption vulnerabilities, allowing attackers to hijack the control flow and take over the system. Existing techniques mostly focus on detecting such attacks but respond by terminating or halting execution upon attack detection, which is not acceptable in CPSs used in safety-critical tasks, as interrupted tasks can have catastrophic consequences. Other techniques replace co

Intrusion Detection Systems (IDSs) are essential for monitoring network traffic and identifying malicious activities in modern cyber-physical, Internet of Things (IoT), enterprise, and distributed network environments. However, developing reliable IDS models remains challenging because attack behaviors evolve over time, realistic datasets are difficult to obtain, traffic records may be incomplete, attack classes are often imbalanced, and privacy constraints limit centralized data collection. Rec

A biology journal that paid peer reviewers found that the approach cut the time to a first editorial decision by 85% and maintained high-quality reviews.

Spatiotemporal point processes (STPPs) model event data in continuous time and space, with applications in mobility, epidemiology, and public safety. Recent neural STPPs span expressive intensity models, conditional density models, continuous-time latent dynamics, normalizing-flow spatial decoders, and score-based generative mechanisms. Yet comparison remains fragile because implementations differ in preprocessing, coordinate normalization, splits, likelihood conventions, and evaluation protocol

Digital forensic investigations of network intrusions require analytical outputs that are traceable, reproducible, and court-defensible - requirements existing machine learning pipelines do not satisfy, since they treat original evidence as training data and produce opaque classifications without instance-level justification. This paper presents a forensic-oriented intrusion detection framework resolving both problems simultaneously, integrating synthetic data generation, binary classification,

The research proposes a multilayer Q-matrix-embedded neural network for cognitive diagnosis (M-QCDNet), which integrates the structural interpretability of cognitive diagnostic models (CDMs) with the deep learning neural network (NN). M-QCDNet structures the item-skill relationship using the Q-matrix as a structural prior, ensuring latent mastery profiles remain interpretable and consistent with cognitive theory, followed by the proposed loss function with an L2 penalty to penalize skills not al

Chuyao is a Ph.D. candidate in Social Research Methods, supervised by Professor Patrick Sturgis and Dr Daniel De Kadt. His research examines how AI governance and generative AI shape political ...