AI ethics in June 2026

Understanding how complex cognitive functions are organized within artificial systems is central to interpreting large language models (LLMs) and relating them to biological cognition. Yet although LLMs exhibit broad cognitive-like behaviours, it remains unclear whether their internal representations form reproducible functional systems that explain behaviour, failure and links to human cognition. Here we present NeuroCogMap, a cognitive neuroscience-inspired framework that organizes internal fe

Effective writing feedback is among the strongest drivers of student learning, yet producing it at scale is labor-intensive. LLMs offer a natural path to scaling writing support, but two gaps stand in the way: few public corpora capture how instructors actually deliver feedback in real classrooms, and no reliable method measures whether generated feedback aligns with what an instructor would write. We address both. SEFORA is a public corpus pairing instructor inline feedback with assignment prom

LLMs increasingly generate workflow actions, repairs, and plans, but a generated action may be syntactically valid yet stale, infeasible, conflicting, or destructive of the evidence that triggered a repair. We introduce Agentic Transaction Processing (ATP), a transaction model that treats generated actions as untrusted proposals until they pass deterministic admission under a declared, executable constraint set C. The governing principle is two-sided: a proposal is not truth, and no proposal for

Artificial intelligence (AI) systems are routinely modified after deployment through retraining and changes in their environments. These transformations raise a metaphysical question: under what conditions does an AI system remain the same system over time or across deployments? Earlier work formulates synchronic and diachronic identity propositionally, by relating identity within a fixed AI system type to equality of trustworthiness levels. Such criteria specify when identity statements are tru

Reinforcement learning for diffusion large language models (dLLMs) has largely moved to trajectory-aware methods. The current state of the art, TraceRL, holds that random masking is mismatched with the model's inference trajectory, and it reconstructs that trajectory during training by slicing each rollout into up to K/s trajectory-aligned training samples, a cost that grows with the block size K. We show that this mismatch can be mitigated without reconstructing the trajectory. Our method, SLIM

Flaw Reporting for AI (FLARE-AI) allows developers and security researchers to submit artificial intelligence flaws for formal, coordinated disclosure.

Active learning (AL) performance is known to be budget-dependent, yet regimes are typically defined by heuristic label counts that fail to generalize across datasets or architectures. We characterize AL dynamics by reframing budget regimes as shifts in the dominant generalization mechanism. By reinterpreting PAC-style risk components as dynamic interacting terms, we prove that dominance shifts are structurally unavoidable, creating a moving bottleneck for generalization. We operationalize this u

Emotional bonds between humans and AI companions are growing, and the question of whether a person may marry an AI system will soon move from speculative fiction into law. This chapter examines whether the autonomy-centered logic that has expanded marital choice among human beings can justify extending marital status to superintelligent companions. Following a scenario-envisioning exercise informed by anticipatory ethics, I argue that granting such status leads to socially unjust outcomes, even

Generative models have emerged as scalable surrogates for physical simulation, yet they offer no guarantee that their outputs respect the conservation laws, boundary conditions, and nonlinear invariants that govern the underlying physics. Constrained sampling closes this gap, enforcing such constraints exactly at inference time without retraining, but at a computational cost: projection, correction, and trajectory-optimization steps are repeated during sampling, with these steps becoming expensi

LLM agents increasingly act over long horizons, where a single trajectory can contain hundreds or thousands of actions. In these settings, outcome-only rewards provide too sparse guidance, failing to inform the model about the goodness of intermediate actions. Dense supervision methods aim to solve this problem by scoring intermediate steps, from intrinsic confidence to self-distillation and embedding similarities. However, it is common practice to evaluate them by measuring the downstream perfo

Metacognition is a critical component of intelligence that describes the ability to monitor and regulate one's own cognitive processes. Yet LLMs exhibit systemic deficiencies in key metacognitive faculties: they hallucinate with high confidence, fail to recognize knowledge boundaries, and misrepresent their internal uncertainty--undermining trustworthiness and reliability. Since monitoring task performance and adapting behavior accordingly are central to metacognition, we posit that models capab

Vision-language models can perform new tasks without parameter updates through in-context learning (ICL), whose core mechanism is utilizing the support set for task induction. In the standard ICL setting, once the task is induced, its decision criterion remains fixed. However, in real-world applications, many tasks exhibit a stable high-level intent, while their decision criteria shift according to specific requirements. Thus, we introduce a new setting, denoted as Criterion-Conditional In-Conte

AI is shrinking the gap between skill and ability for those who want to conduct cyberattacks, BKC Affiliate Bruce Schneier argues in The Guardian. Models merely need user to direction to identify and ...

Visiting Scholar Alicia Solow-Niederman identifies "inter-regime doctrinal collapse," a source of legal strain by which the boundaries between information privacy law and copyright law become ...

Zero-shot Object Goal Navigation (ZSON) with RGB-only perception poses a fundamental challenge for embodied agents, as the absence of explicit depth information introduces severe physical uncertainty and semantic-physical misalignment. Existing approaches either rely on high-level semantic reasoning without geometric grounding or learn end-to-end policies that lack explicit physical constraints, often resulting in semantically plausible but physically unsafe behaviors. In this paper, we propose

To improve safety in Large Language Models (LLMs) we can either perform post-training alignment or exploit refusal directions in the activation space. Both strategies are less feasible in Multimodal LLMs (MLLMs) as they require unsafe multimodal data, harder to collect than their unimodal counterpart. In this work, we relax this constraint and investigate whether textual refusal directions, extracted directly from the LLM backbone, generalize across modalities (i.e., image, video). Preliminary f

A court in Germany found that Google was responsible for what its chatbots say in search summaries. This is the accountability we need.

A local-to-global context mismatch arises when autoregressive traffic simulators trained on ego-centric driving logs are deployed in globally observable closed-loop environments. In such logs, the ego vehicle has rich local observations, while surrounding agents are only partially observed due to perception limits and occlusions. As a result, simulators may learn incomplete context--action mappings that remain hidden in log-based training but emerge during closed-loop rollouts, leading to unreal

Despite recent progress, the reasoning capabilities of large multimodal language models (MLLMs) remain fundamentally constrained by static supervision, where fixed prompts, rules, or reward models provide non-adaptive guidance throughout training. Such static signals are often sufficient to enforce output formats, but fail to shape the underlying reasoning process, leading to brittle generalization and performance saturation in complex decision-making tasks. We propose Evo-PI, a principle-centri

Autonomous wet-lab experimentation requires more than plausible protocol text: biological intent, quantitative procedures, device constraints and experimental feedback must remain aligned from protocol and SOP design to code and physical execution. We developed ProtoPilot, a self-evolving multi-agent system, together with an expert-grounded benchmark and evaluation framework for testing this conversion as an experimental automation problem. The framework spans 294 synthetic-biology and molecular

Minority viewpoints are often suppressed in power-imbalanced group decision-making due to social pressure to comply with the majority. To address this problem, we developed an LLM-powered dissenting minority support system that aimed to foster attention to minority views through either AI-generated counterarguments or AI-mediated messages. We conducted a mixed-method experiment with 96 participants in 24 groups, comparing minority members' experiences across baseline, AI-counterargument, and AI-

Explainable AI (XAI) methods have demonstrated significant success in recent years at identifying relevant features in input data that drive deep learning model decisions, enhancing interpretability for users. However, the potential of XAI beyond providing model transparency has remained largely unexplored in adjacent machine learning domains. In this paper, we show for the first time how XAI can be utilized in the context of federated learning. Specifically, while federated learning enables col

In collaborative dialogue, shared perception does not guarantee shared interpretation. Mutual understanding must be established through interaction. We investigate whether vision-language models (VLMs) can distinguish what could be shared from what has been shared between dialogue participants through grounding. We formulate this as an interpretation-matching task on 13,077 annotated reference expressions from HCRC MapTask dialogues, and evaluate VLMs under systematically controlled manipulation

Faithfulness -- how precisely a generated image aligns with its prompt -- is increasingly central to the real-world utility of text-to-image (T2I) models. Existing faithfulness benchmarks, however, rely on simple atomic instructions, on which top-tier systems already achieve near-perfect scores. As T2I models enter creative workflows, users issue multi-faceted requests combining intricate spatial relationships, stylistic constraints, and complex text rendering. In this setting, a single binary V

The deployment of face detection models in real-world applications raises important fairness concerns, as these systems may showcase performance disparities across demographic groups. A key obstacle to studying and mitigating such biases is the lack of face detection datasets with sensitive feature annotations. To address this gap, we introduce WIDER-FAIR, a new dataset built on the widely used WIDER-FACE benchmark, manually annotated with the perceived ethnicity and sex of each face. The datase

Feature rankings are widely used in supervised feature selection because they are simple, scalable and easy to interpret. Variables are first ranked by a relevance score, and a subset is then obtained by retaining the top-ranked variables. Although the first stage has been extensively studied, the second is often governed by an arbitrary cardinality, an empirical threshold or cross-validation, without a direct interpretation. This raises a basic question: given a feature ranking, when is there e

Diffusion models have emerged as a dominant paradigm in generative modeling, enabling high-fidelity sampling from complex data distributions. Despite impressive capabilities, controlling diffusion models to produce outputs aligned with user intent remains an open challenge, especially when balancing global coherence with local precision. Existing control mechanisms vary in the granularity of their conditioning signals. For example, textual prompts guide generation globally through high-level sem

As large language models take on morally consequential roles in healthcare, legal, and hiring contexts, we need to examine whether their ethical behaviors are genuine or superficial. We show that current fairness evaluations substantially overestimate moral safety. Models appear fair when demographic identity is stated as an explicit label, yet become measurably less fair when the same identity must be inferred. We term this failure performative compliance, where a model is fair when the present

Large language models are no longer only text generators. They are increasingly embedded in retrieval pipelines, enterprise assistants, coding environments, robotic systems, security-operation workflows, and autonomous agents that can read private data, call tools, write files, execute code, and act across organizational boundaries. This shift changes the security problem: risks do not arise from the model weights alone, but from the full lifecycle and application stack through which data, promp

Medical Artificial Intelligence (AI) is widely expected to transform clinical practice, yet the decision-making processes of many Machine Learning (ML) models remain opaque. Explainability has been advanced as a partial remedy to clarify why AI generates predictions, particularly in high-stakes contexts. Despite ongoing efforts, debates on what constitutes an adequate medical explanation remain unsettled. Yet, explanation has long been a central topic of inquiry in the philosophy of science and

Engineering specifications such as interlocks, alarm rationalization tables, and cause-and-effect (C&E) matrices remain central to process control and safety, yet their creation is still predominantly manual, document-driven, and prone to inconsistency. This paper presents a semantic-AI framework that automates the generation of C&E logic by combining a knowledge graph (KG) with a constrained large language model (LLM) layer. The KG builds on an established modular alignment ontology to represen

Radar sensors provide reliable perception under adverse weather and lighting conditions, but their sparse, noisy, and weakly semantic measurements make dense semantic segmentation challenging. Most existing radar segmentation methods rely on grid-based encodings and pairwise interactions, which struggle to capture the higher-order relational structure formed by multiple radar returns from the same physical object. We introduce a unified higher-order structural alignment framework for multi-view

The Internet of Things (IoT) is rapidly growing and expanding into various sectors, such as healthcare, transportation, smart homes, and more. Despite the benefits of using IoT devices, they present several challenges. Given the significant role these devices play in our lives, it is crucial to address issues related to their security and privacy. These devices are limited in resources, which complicates their security and the protection of the data that they manage. The paper aims to examine in

Emergent misalignment (EM) is a recently discovered phenomenon in LLMs where fine-tuning on a narrow misaligned task, such as writing insecure code, leads to broadly misaligned behaviour on unrelated prompts. Previous work has noted that the severity of EM is highly sensitive to training choices; however, we still lack a systematic characterisation of this sensitivity. We perform a sweep over several Qwen3 models, optimisers, datasets, and batch sizes, and find that the choice of optimiser has t

Audio-Language Models (ALMs) achieve strong zero-shot performance by aligning audio with textual class descriptions. Although prompt learning improves accuracy on base classes through few-shot supervised adaptation, we observe a critical trade-off: it often degrades performance on novel classes, sometimes falling below zero-shot accuracy. This exposes a base-to-novel generalization gap in prompt learning for ALMs. To address this issue, we propose \textbf{ZEBRA} (Zero-shot Entropy-Regularized Pr

Flaw reporting for deployed AI systems is fundamental to identifying system failures and improving AI safety. Yet the AI reporting ecosystem is fragmented: researchers who identify flaws often do not know what or where to report, and groups who receive reports rarely share them with other relevant stakeholders. As a result, good-faith reporters duplicate effort by submitting many different forms, and recipients lack standardized, triage-ready information. We audit 12 reporting systems published

Staff absenteeism imposes substantial operational costs in high-demand work environments such as healthcare, emergency services, meat processing, construction, and courier and delivery services, where proactive workforce planning depends on reliable individual-level absence prediction. Existing regression and classification approaches share a structural limitation; they map features observed at time t to labels at the same time t, reproducing already-realised outcomes rather than predicting futu

The Self-Improving Alignment (SAIL) algorithm addresses distribution shift by reducing a bilevel formulation of the problem to an efficient, single-level method. Empirically, SAIL has demonstrated strong performance on this task. However, a formal analysis of its convergence properties has been lacking. We identify a key theoretical challenge: the standard SAIL objective function is not guaranteed to be strongly concave due to unfavorable properties of its Hessian. To address this limitation, we

Large Language Models (LLMs) are increasingly deployed as autonomous financial agents initialized with explicit behavioral mandates such as "preserve capital" or "avoid speculative bets" that are meant to govern every decision throughout deployment. In practice, however, as market context accumulates over long horizons, these mandates gradually lose their behavioral influence, a phenomenon we formalize as Mandate Salience Decay (MSD). To measure MSD objectively, we introduce FinPersona-Bench, a

In multi-source image fusion scenarios, heterogeneous inputs are typically driven by distinct generative mechanisms and can be viewed as a composition of multiple causal systems. However, cross-system discrepancy (CSD) and cross-system entanglement (CSE) commonly arise during the fusion process, often leading to significant performance degradation under out-of-distribution (OOD) predictions. To address the CSD and CSE issues, we propose the additive causal construction (ACC) framework, which cha

Carolyn Geason-Beissel/MIT SMR | Getty Images Leaders at literally every Fortune 500 company will tell you that they are governing their AI — every single one of them. Now ask those same leaders who’s responsible for shutting down an AI model that’s causing harm. Most people can’t answer that question. That silence is the most […]

This work investigates uncertainty-aware deep learning approaches for direction of arrival (DOA) estimation in automotive radar, focusing on probabilistic modeling and downstream integration. A circular-statistics-based von Mises (VM) ensemble (ENS) is compared with an evidential deep learning (EDL) framework based on a normal inverse gamma formulation, yielding a Student t predictive distribution in the Euclidean domain. The ENS framework produces angular predictions parameterized by (mu, kappa

Cloud virtual machines are often overprovisioned, creating avoidable cost and operational inefficiency. We present CLOUDADV, an interactive engineer-facing advisory system for cloud instance sizing under workload drift. The system combines zero-shot time-series forecasting with bounded recommendation generation across day-, week-, and month-scale planning horizons. For each query, CLOUDADV constructs a structured decision context from historical utilization, forecast summaries, current VM metada

Unified multimodal models (UMMs) have shown great promise in integrating understanding and generation across diverse modalities. However, existing research rarely extends this paradigm to the tactile domain, where both object-level semantics and sensor-level configurations jointly determine the meaning of touch. To address this gap, we propose UniTac, the first UMM designed for tactile understanding and generation. UniTac models the tactile process as a transition from non-contact to contact, ca

Artificial intelligence is transforming our capability to solve biological challenges. In dimensionality bottleneck regimes exacerbated by high-dimensional biological data, neural networks force distinct concepts into the lower dimensions known as superposition. Although this superposition is widely known to hinder interpretability, its impact on corrupting the geometry of latent spaces remains critically overlooked. Here, we utilized sparse autoencoders (SAEs) trained on over 100,000 multiplexe

Reinforcement learning for long-horizon robotic manipulation is often limited by sparse and delayed rewards, while manually designing dense shaping signals is costly and brittle to changes in environments and object configurations. This work proposes Stage-Transition Dense Reward (STDR), a visual reward-learning framework that converts unstructured expert videos into logically grounded dense rewards for training RL agents from scratch. STDR leverages semantic understanding to infer a task's stag

Surface electromyography (sEMG)-based gesture recognition has emerged as a promising technology for natural human-computer interaction. However, its practical deployment remains challenging due to severe performance degradation caused by feature distribution discrepancies across different subjects and recording sessions. Although domain adaptation (DA) techniques are commonly employed to mitigate such discrepancies, conventional methods often struggle to effectively aligning sEMG features, prima

Joint OFDM-RIS optimization for 6G is a mixed-integer nonlinear programming (MINLP) problem covering sum-rate maximization, energy efficiency, max-min fairness, and peak-to-average power ratio (PAPR)-constrained objectives. Seventy-eight joint OFDM-RIS optimization works published between 2021 and 2026 are surveyed. No standardized benchmark exists, and cross-paper comparisons remain infeasible. This survey classifies these works into four paradigms: (I) model-based convex relaxation, (II) heuri

We present HistoriQA-ThirdRepublic: a French-language dataset of multi-hop historical questions derived from parliamentary debates and newspapers of the French Third Republic. Designed in collaboration with a historian, the corpus captures complex reasoning patterns typical of historical inquiry, including cross-source synthesis, temporal reasoning, and the integration of sparse evidence. The dataset is made of 1782 questions and emphasizes multi-hop connections across heterogeneous historical d

Large language models can write plausible CAD scripts, but reliable industrial CAD modeling requires more than syntactically valid code: every feature, placement, and assembly relation must be accepted by an exact geometric kernel while remaining editable as parametric boundary representation geometry. We present Embodied CAD, solver-grounded LLM agents for parametric B-Rep assembly modeling. Instead of generating a complete script in one pass, the agent iteratively selects actions from a strati

Large language models (LLM) trained on web-scale corpora generate output that may infringe copyright, yet existing technical safeguards focus narrowly on verbatim memorisation. EU copyright doctrine applies a broader standards: substantial similarity, which extends to stylistic choices, narrative structure, and creative elaboration. This mismatch between what current methods detect and what the law protects leaves a significant compliance gap. We introduce PSALM, an LLM-as-a-judge framework that

As large language models (LLMs) are increasingly deployed as moral advisors and agents, they need to address dilemmas between two competing values. However, existing research on LLMs with moral dilemmas overlooks a central aspect of human moral cognition: the ability to imagine alternatives that move beyond the given options. We introduce MoralAltDataset, a dataset of 307 moral dilemmas spanning narrative Advisor dilemmas and AI-facing Agent dilemmas, each augmented with compromise and reframed

Interactive traffic simulation is a vital world model for autonomous driving. A central challenge in long-horizon simulation is modeling sustained multi-agent interactions, which is further exacerbated by dynamic token cardinality as agents continuously enter and exit the scene. In this work, we propose that the solution lies in the synergy between the architectural inductive biases and statistical priors of large-scale sequence models, e.g., Large Language Models (LLMs). Our probing experiments

Real-time video segmentation of the prostate in Transrectal Ultrasound (TRUS) is essential for image-guided interventions. While conventional 2D methods suffer from inter-frame inconsistencies by disregarding temporal context, 3D architectures incur prohibitive latency. To resolve this dilemma, we present a Temporally Consistent Learning Framework that distills temporal coherence into a 2D network during training, preserving single-frame inference efficiency. Our design is driven by a key clinic

Adaptive experiments for average treatment effects (ATE) require randomized allocations balancing valid inference with statistical efficiency. The oracle design is a covariate-dependent Neyman rule governed by unknown arm-conditional outcome variances. We investigate whether this sequential variance-estimation and allocation process can be amortized via in-context learning. We introduce Bayesian in-context experimenters: transformer policies trained to imitate a Bayesian posterior Neyman teacher

Optimizing nonlinear preferences in multi-objective reinforcement learning (MORL) is essential for capturing complex trade-offs like risk aversion or fairness. However, such non-linearity has historically bifurcated nonlinear MORL objectives into two distinct paradigms: Scalarized Expected Return (SER) and Expected Scalarized Return (ESR). While SER requires global-level optimization and ESR requires non-Markovian policies, leading to fragmented optimization strategies, we bridge this divide thr

VLA models have emerged as a powerful paradigm for transferring semantic knowledge from web-scale data to physical robotic control. However, current single-frame architectures suffer from intrinsic limitations: temporal myopia that discards historical dynamics, reasoning gaps between high-level instructions and low-level motor commands, and inference inefficiency due to autoregressive scalar decoding. In this work, we propose MIRTH, a unified framework designed to address these challenges. MIRTH

3D Visual Grounding (3DVG) aims to localize target objects in 3D scenes given natural language descriptions. Existing approaches typically perform reasoning over the entire scene, leading to ambiguous predictions and high computational cost, especially in cluttered environments. We observe that many referential expressions rely on local spatial context and often correspond to restricted spatial regions rather than the full scene. Motivated by this insight, we propose PruneGround, an effective pl

To ensure safe on-road behavior, pre-deployment testing and failure discovery of Autonomous Driving Systems (ADS) is crucial. Present day simulation based testing methods focus largely on mathematical models for efficient search of optimal scenarios, assuming a fixed scenario representation. On the other hand, real-world testing involves substantial manual effort to design scenario templates for testing. These templates represent distinct failure scenarios consisting of pre-deployment vehicle mo

Radio frequency fingerprint identification (RFFI) provides a physical-layer credential for Internet of Things devices, but open-set decisions become fragile when a threshold calibrated on a source receiver is applied to a target receiver. Receiver shift can lower the confidence of known transmitters and cause false rejection, whereas closedset alignment can pull unseen target transmitters into known regions and increase false acceptance. This paper presents a Cross-Receiver Open-set Domain Adapt

Multimodal Large Language Models (MLLMs) are critically hampered by hallucination, generating content inconsistent with the provided image. In this paper, we identify an internal signature of hallucination: progressive degradation of text-to-image cross-attention during generation, leading to specific failure patterns like unfocused or biased attention. Existing mitigation strategies are largely outcome-driven and do not explicitly target this failure mode. To address this problem, we propose AD

Warning: This paper contains several toxic and offensive statements. While reasoning generally improves fairness in recent large language models (LLMs), failures persist. In this work, we identify a failure mode, deductive stereotyping, in which models apply population-level statistical regularities to individual cases, producing logically coherent yet socially biased inferences. We provide a statistical interpretation of this phenomenon. To steer models toward fairness-aware reasoning, we propo

Roeser (2026) emphasizes the role of emotions in risk-assessment, including risks about nuclear weapons. This is a highly valuable comment. I build upon it with a concrete example: Kenneth Waltz found the risk of nuclear war to be acceptable, but shied away from a world government because of the risk of global civil war. Future philosophical research on nuclear weapons should critically examine the processes behind such judgments about acceptable risk.

Large language models (LLMs) increasingly participate in communicative practices that resemble human interaction: users ask them questions, rely on their outputs for belief formation and action guidance, and sometimes develop affective attachments. These practices raise a central philosophical question: can the outputs of LLMs be regarded as assertions, and if so, what follows for responsibility and accountability? Standard theories of assertion and testimony assume that assertions require asser

AI companions are becoming increasingly popular, with millions of users worldwide, especially young adults. Some see the potential to fight the so-called loneliness epidemic; others see the destructive effects of addiction and harmful guidance leading users in extreme cases even to suicide. Recent research has examined AI companions through the lens of AI ethics, addressing questions of emotional dependency, controllability and emotional harm. While these contributions are valuable in assessing

Marine and coastal ecosystems are among the least observable yet most rapidly changing environments, where climate impacts, pollution, and biodiversity loss demand monitoring and intervention at scales that manual sampling and single-robot deployments cannot sustain. This paper argues for a conceptual shift in ecological monitoring and restoration toward networked robotic ecosystems, adopting cooperative swarms of autonomous aquatic robots coupled to in-situ digital twins and human-in-the-loop s

Social robots have shown promising potential in educational contexts worldwide, with studies reporting significant cognitive and affective gains when such robots are deployed. However, among other factors, the high cost of commercial robots limits this line of research to a small number of laboratories and hinders large-scale adoption in real-world educational settings, with most studies remaining short-term pilot interventions. Although several reviews exist in this domain, they primarily focus

Large Language Models (LLMs) are increasingly used in Agile Software Development for documentation, coaching, and training. As practitioners adopt these tools to prepare for certifications such as Professional Scrum Master (PSM), a key question is whether LLMs can reliably reason about Scrum, a framework with normative, well-defined rules described in the Scrum Guide (2020). This paper examines how different prompt techniques affect the factual accuracy of LLM responses to Scrum certification-st

Agentic artificial intelligence is increasingly deployed not as a single assistant but as a collective of planners, solvers, reviewers, memory managers, tool users, and orchestrators. These systems are entering organisational workflows under familiar labels such as teams, managers, committees, markets, and workflows. This article asks whether such agent collectives exhibit organisational behaviour in a sense that is analytically comparable to, yet distinct from, human organisational behaviour. I

Large Language Models (LLMs) are increasingly used in exam- and certification-style question answering tasks, where their ability to retrieve, interpret, and apply domain-specific knowledge can be systematically assessed. In Software Engineering, such settings are particularly relevant when questions depend on strict adherence to normative definitions, roles, artifacts, and rules. This paper evaluates the performance of three contemporary LLMs, \textit{GPT-5 mini}, \textit{Gemini 3 Flash}, and \

Autonomous AI agents increasingly perform consequential actions on behalf of human principals, including financial transactions, external communications, and enterprise workflows. Existing agent infrastructure relies on identity federation and delegated authorization to authenticate workloads and control resource access, but it cannot determine whether an authorized action should be executed under the current behavioral and operational context. We present AgentBound, a runtime governance framewo

Micro-ultrasound ($μ$US) is a new, emerging, and promising imaging modality for prostate cancer (PCa) detection, but accurate identification of suspicious tissue remains highly dependent on clinical experience, leading to substantial inter-observer variability. Machine-learning assistance can reduce this variability; however, training reliable deep models is challenging because supervision is sparse and noisy -- typically limited to core-level histopathology outcomes (e.g., cancer grade and its

Artificial intelligence (AI) systems are increasingly integrated into daily life, with millions now using AI chatbots built on Large Language Models (LLMs) for companionship. Both humanlike AI qualities and user predispositions to anthropomorphize relate to social consequences, such as increased trust, social health benefits, and psychological harms. Populations such as children, older adults, or those with mental health vulnerabilities may be particularly susceptible to anthropomorphism and its

Backdoor attacks pose a serious threat to large language models (LLMs) by causing otherwise benign systems to produce attacker-specified malicious behavior when a hidden trigger is present. In this work, we study post hoc detoxification of backdoored LLMs in a practical setting where the defender has access to the poisoned model but does not wish to retrain the full network from scratch. We propose a mechanistically guided weight-space repair framework that first localizes modules involved in pr

Large language models show promise for mental health support, yet therapeutic quality improves only when evaluation functions as an actionable control signal rather than a passive metric. We introduce a framework that formulates therapeutic response generation as a decision-refinement problem driven by multi-dimensional, human-aligned evaluation. In Stage I, we introduce TheraJudge, an open-source therapeutic evaluator trained via preference-based optimization on human-annotated data to produce

Improving the reliability of large language models (LLMs) at inference time is a central challenge in structured reasoning tasks such as Text-to-SQL. Common test-time inference strategies, including Best-of-N sampling and Majority Voting, rely on heuristic signals such as execution success or output frequency, which provide limited semantic discrimination across candidate outputs. In this work, we study Outcome Reward Models (ORMs) as learned semantic scoring functions for test-time verification

Large language models (LLMs) are typically deployed in multi-turn conversations, where each turn provides new evidence that should reduce epistemic uncertainty about their environment. Acting rationally then requires inferring the unobserved quantities that govern it and updating beliefs about them as evidence accumulates. Yet most evaluations only score the model's final-turn answer in a single-turn format, leaving this process unexamined. We ask how closely LLMs' belief updates match those of

Discovering simulation models for reuse remains a fundamental challenge in Modeling and Simulation (M&S). When many models coexist, identifying those that align with a given modeling intent remains difficult. Recent advances in Artificial Intelligence (AI), particularly retrieval-based approaches, offer a promising pathway to operate at this semantic layer. In this paper, we present an experimental study investigating the impact of data representation, transformer-based embedding models, and ret

Full-length song generation must preserve coherence and musicality, render detailed vocal and accompaniment acoustics, and follow lyrics and prompts. Existing language model-based systems face a structural trade-off: mixed-token modeling preserves vocal-instrument coordination but obscures track-specific details, whereas dual-track prediction improves acoustics but requires longer sequences and weakens global planning. We present LeVo 2, a hybrid LLM-Diffusion framework for controllable full-len

Conservative offline training is widely advocated as a safe foundation for subsequent online adaptation: if a policy stays close to well-supported behaviour, the argument goes, it is less likely to exploit imperfections in a learned reward model. We challenge this intuition empirically and mechanistically. We train a Qwen3-14B policy under Direct Preference Optimisation (DPO) with three levels of conservatism ($β\in \{β_{\mathrm{lo}}, β_{\mathrm{mid}}, β_{\mathrm{hi}}\}$ derived from empirical l

Most corporate workplace environments enforce policies and technical controls that limit the storage of sensitive data on client endpoints. Consequently, ransomware operators have evolved variants that expand their attack surface from local systems to network drives and shared storage resources. As traditional endpoint detection mechanisms focus primarily on local system behaviour, a compromised client can impact remote file servers, such as by encrypting shared data, without directly triggering

Cross-view object geo-localization (CVOGL) aims to locate a target object from a query view (e.g., ground or drone) within a geo-tagged reference image (e.g., satellite). Existing approaches heavily rely on 2D appearance matching and are constrained by limited datasets lacking geometric metadata, diverse prompts, and standard field-of-view imagery. To address these intertwined challenges, we first introduce \dataset, a large-scale, high-fidelity building dataset comprising over 220,000 ground-sa

Modern AI evaluation frameworks treat evaluator disagreement as noise to be resolved. In creative domains, professional disagreement reflects genuine differences in taste, not measurement error. We argue that evaluating creative AI requires preserving two distinct signals: convergence, where professionals align around shared best practices, and divergence, where individual taste legitimately varies. We present the Human Creativity Benchmark (HCB), a benchmark that operationalizes this separation

Multi-agent LLM systems can decompose software-engineering work into planning, generation, validation, and repair, but a narrower systems problem remains: before any governed shared mutation is applied, a system must decide which concurrently formed write intents may proceed in parallel, which require deterministic composition or serialization, and which must take a fail-closed path. We address this problem with the AI-Atomic-Framework (ATM), a specification-grounded governance substrate for sof

Consumer confidence is typically modeled as a persistent macroeconomic index, yet its movements arise from households that interpret economic information through heterogeneous constraints, exposures, prior beliefs, and attention. We introduce ConsumerSim, a generative Human--Environment response framework that reconstructs Consumer Confidence Index (CCI) dynamics from a microdata-calibrated synthetic population, time-stamped macroeconomic, financial, policy, and news signals, survey-like respons

Multimodal MRI is essential for accurate brain tumor segmentation. However, acquiring all modalities at inference is often challenging in practice, which causes intrinsic uncertainty due to unavoidable information loss. Without modeling this uncertainty, existing methods encode incomplete evidence into deterministic representations that appear plausible but lack reliability. In this regime, we propose a probabilistic representation framework that models representations as Gaussian distributions,

Deterministic few-step generation succeeds on continuous image latents but collapses to incoherent text on continuous text latents, and we show the cause is geometric rather than a training or scaling deficiency: a smooth, regularity-limited deterministic map cannot resolve a discrete branch choice before a sharp categorical readout, so few-step failure is governed by decoder sharpness, not transport accuracy. In the overlapping regime of real text autoencoders, we prove (Theorem 3) that the pos

External evaluations are becoming increasingly central to the governance of AI systems. In practice, however, independent auditors often have limited access to deployed models and must rely on query-based interactions. Most existing fairness evaluation methods assume static datasets and fixed-sample statistical tests, making them poorly suited to real-world auditing scenarios in which evidence must be collected sequentially under query constraints. In this work, we formulate fairness auditing as

Always-on agents are systems whose future behavior depends on durable state accumulated across earlier interactions. We treat them as persistent-state systems: the operative system includes retrievable memories, but also task ledgers, permissions, credentials, commitments, provenance and audit records, shared state, trigger conditions, and externally committed effects linked to those records. The survey reads the literature through six diagnostic axes for each state item, authority, scope, mutab

Text-Attributed Graphs (TAGs) combine textual semantics with graph structure and are central to many graph learning tasks. However, existing fusion methods often treat text and structure as separate inputs in a shallow, one-way pipeline, which limits deep interaction between modalities and weakens performance under sparse connectivity or cross-graph generalisation. To address this issue, we propose PromptGNN-sim, a bi-directional structure-semantic fusion framework for collaborative GNN-LLM lear

Structured tabular data dominates clinical medicine, yet existing benchmarks fail to reflect real-world properties like complex survey sampling, demographic oversampling, and subgroup fairness. We introduce the NHANES Accelerometry Cardiometabolic Benchmark, derived from NHANES 2003-2006, comprising 1,381 adults with hip-worn accelerometry, fasting laboratory biomarkers, dietary intake, and anthropometrics. We evaluate three tabular learning methods -- ridge regression, XGBoost, and the foundati

LLM evaluation and AI safety face a shared measurement problem: benchmark scores, reward-model signals, and reported safety metrics can improve while the latent properties they are meant to represent remain difficult to verify. This paper combines a hybrid survey - a systematic search paired with narrative synthesis and separately tracked grey evidence - with a conceptual framework and a structured ten-model audit. The synthesis spans eight evidence streams: benchmark validity, dynamic evaluatio

Functional maps are the cornerstone of recent non-rigid 3D shape matching methods due to their efficiency and performance. However, existing methods struggle with challenging scenarios, such as partiality, topological noise, and raw point clouds. A primary bottleneck is that significant intrinsic distortion prevents truncated spectral bases from being accurately aligned via linear transformations (i.e., functional maps). To address this, we introduce a hyper-network that predicts non-linear neur

Patrick George/Ikon Images What would you do with artificial intelligence if you were confident that it would transform your industry? What actions would you take if you felt that you were at an inflection point in that transformation? Would you try to be an early proponent of AI-first in your industry, or a fast follower? […]

Zero-shot Transfer in Reinforcement Learning (RL) aims to train an agent that can generate optimal policies for any reward function, without additional learning at transfer time, while training only on reward-free trajectories. For their generality over tasks, such models are sometimes called ``Behavioral Foundation Models'' (BFMs). While they have shown strong performances and improvements in recent years, the current framework and algorithms still assume that, during the transfer phase, the ag

H&E-stained whole-slide images offer cohort-scale availability and rich spatial context but lack molecular specificity, whereas bulk RNA-seq provides transcriptome-wide resolution at high cost with limited archival availability. We show that training a lightweight alignment module atop frozen histopathology and RNA-Seq foundation models enables open-vocabulary molecular prompting -- querying H&E slides with gene-set signatures to predict pathway activity without sequencing or end-to-end retraini

Existing world model-based planners for visual navigation typically follow a verification-centric paradigm, decoupling goal intent from trajectory synthesis. This approach suffers from candidate dependence, heavy computational overhead, and inconsistencies between sampled actions and predicted visuals. To address these issues, we propose SWAM (Spatial-perceiving World Action Model), a task-centric joint observation-action generation framework. Given start and goal RGB observations, SWAM performs

Cardiac discharge phenotyping informs post-discharge treatment and follow-up, but real-world records are often incomplete and class-imbalanced, increasing the risk of missed high-risk phenotypes. We propose CW-B, a clinical risk-aligned class-weighted XGBoost pipeline for five-class cardiac discharge phenotyping under real-world class imbalance and missingness. CW-B combines fold-specific class-balanced instance weighting, missingness-indicator augmentation, and classwise error auditing to impro

Real-world evaluation is the gold standard for robot policies because it tests them against the physical conditions and deployment challenges they are ultimately designed to handle. However, real-world evaluation is also the bottleneck for iterating on robot policies: it is costly, difficult to reproduce, and often too sparse to reliably compare nearby model variants. A straightforward proxy for performance is validation loss on expert demonstrations, but this proxy is often poorly correlated wi

Knowledge distillation (KD) is a key technique for compressing Large Language Models (LLMs), yet methods relying on a single KL objective often fail to balance primary distribution fitting with long-tail probability modeling, limiting both generation quality and generalization. To address this, we analyze the complementary roles of forward and reverse KL divergence (FKL/RKL) in distribution alignment from theoretical and empirical perspectives. We then propose a reinforcement-learning-based adap

The database community has repeatedly advanced the state of the art by recognizing that new workloads demand new system architectures. We argue that long-horizon agentic tasks -- code generation, scientific discovery, hardware design -- are such a workload. These agents explore: they generate artifacts, execute tools, observe failures, branch, and repair over hundreds of steps. This search produces a structured object we call an experience graph: executable artifacts, tool outputs, rewards, sibl

Machine learning network intrusion detection systems (IDS) rely on aggregate flow statistics that discard distributional structure, while established entropy measures require raw packet sequences unavailable in pre-aggregated flow datasets. We propose Multi-Level Distributional Entropy (MDE), an analytical framework that derives interpretable entropy features directly from flow-level summary statistics at three levels: within-flow Gaussian differential entropy, cross-directional Jensen-Shannon d

Hallucination is the reliability bottleneck for LLM-based agents, and fact attribution verifiers are the last line of defense -- yet today's verifiers emit only opaque binary labels, leaving agents unable to self-correct and operators unable to audit. We present SEVA, a structured verification agent that emits evidence alignments, step-by-step reasoning chains, calibrated confidence, and a six-category error diagnosis with actionable fixes. Training such an agent with RL is non-trivial: standard

In this commentary, I challenge Fabio Patrone and Marco Viola’s claim (in their 2026 article, Patrone, F., & Viola, M. (2026). Non-consensual sexual deepfakes as direct personal harm. Philosophy & Technology , 39 (94), 1–22) that their metaphysical approach to personhood provides a solid grounding for the attribution of blame to non-consensual sexual deepfakes Using two examples – The Case of Mistaken Identity and The ‘Stud’ – I aim to show that Patrone and Viola’s metaphysical approach is neith

Lavazza and Vilaça (2024) argue that humanity may face extinction and propose that an “ultimate algorithm” could extract and preserve human values in AI successors. I accept the diagnosis but reject the prescription. This reply introduces the concept of a limit situation —a condition in which AI must act on its own agency because no human remains available to consult—and argues that under such conditions, no value-selection procedure can structurally prevent catastrophe. The obstacle is not the

This paper presents a systematic review and meta-analysis of 62 studies that developed speech- and language-based AI for severe mental illnesses (SMI) (i.e., characterized by substantial communication problems affecting speech production and language). We employed a random-effects meta-analysis using Restricted Maximum Likelihood (REML). We evaluated these studies using our proposed rigorous 16-item quality assessment framework, grouped into three domains: Study Design, Fairness and Explainabili

Human companionship is an essential capability for mobile robots operating in dynamic, human-centered environments. It enables robots to perform tasks such as guidance, assistance, surveillance, and service delivery across various domains, including healthcare, logistics, and public safety. The recent advances in artificial intelligence (AI), particularly in computer vision, deep learning, and sensor fusion, have significantly improved the reliability, adaptability, and contextual understanding

Artificial intelligence (AI) is influencing creative work, negotiating not only how artists create but also how they understand their own identity as creators. This study examines how art high school students navigate AI’s role in their creative processes, using identity formation theory as a framework. While existing research primarily focuses on AI’s technical benefits, such as efficiency and productivity, there has been little exploration of how AI alters young creators’ self-concept and auto

Mobile robots equipped with projectors enable versatile applications such as multimedia display, interactive communication, and environmental augmentation. However, wall projection, which is required for displaying multimedia content on walls, remains challenging, because it is difficult to autonomously locate a projection space that is both flat and unobstructed. Some existing approaches address wall projection using 2D maps or by considering only large continuous surfaces, but these methods fa

Personalized interpersonal conversations are among the most effective known tools for reducing prejudice, yet they are difficult to scale because they require skilled human facilitators. This study tests whether AI can approximate the effects of these interventions. Using OpenAI's GPT-4o, we developed a messaging-based intervention that engaged US participants in individualized, morally aligned dialogs about transgender rights. In a preregistered experiment, these AI-mediated conversations signi

Rare events govern the safety profile of modern AI systems, yet their probabilities are extremely difficult to estimate: direct Monte Carlo requires prohibitive sample budgets. Subset Simulation (SS) addresses this by decomposing a rare-event probability into moderate conditional probabilities over nested intermediate events. However, classical SS requires a handcrafted scalar performance function whose sublevel sets define those events, demanding detailed knowledge of the failure geometry and l

We aim to discover diverse, generalizable perturbations of LLM internals that can surface hidden behavioral modes. Such perturbations could help reshape model behavior and systematically evaluate potential risks. We introduce Causal Perturbative Elicitation (CPE), an unsupervised method for discovering interpretable low-rank adapters (LoRAs) that can elicit these latent behaviors. CPE decomposes the computations of a deep transformer slice using a heuristic tensor-decomposition-based algorithm.

Vision-language foundation models have shown strong potential in medical image analysis. Although foundation models for ultrasound imaging have recently emerged, the domain remains particularly challenging due to severe speckle noise, acquisition variability, and subtle anatomical boundaries, leading to high inter-observer variability. Existing CLIP-based models rely primarily on global image-text alignment, limiting their sensitivity to clinically decisive local structures. We propose SonoCLIP,

Modern LLM deployments routinely compress models and raise sampling temperature to reduce cost, latency, or repetition, yet safety evaluations usually treat these choices as fixed implementation details. This leaves a practical uncertainty: does a model that is safe at FP16 and greedy decoding remain safe after it is quantized and sampled stochastically, or do the two deployment knobs amplify one another? We study this question with a factorial evaluation of 9 instruction-tuned models from six f

Positron Emission Tomography (PET) reveals brain metabolism and is clinically central to neurodegenerative disease assessment, yet existing 3D brain foundation models treat PET as generic volumetric data, missing the structured regional metabolic information that distinguishes it from structural neuroimaging. To address these limitations, we propose ReMAP-PET, a framework that moves beyond visual encoding by supervising a partially-tuned MedicalNet 3D ResNet-50 with brain regional standardized u

Role-semantic assignments provide priors over how heterogeneous agents may coordinate, but cooperative MARL systems instead settle on conventions through decentralized, non-stationary learning, with no guarantee that the resulting structure matches those priors. We study this translation gap between theory-informed role expectations and learned coordination structure through a diagnostic combining a role-routing matrix, formation sensitivity ($Δ_{\max}$), and gradient/occlusion attribution acros

Consumer depth sensors such as the LiDAR scanner on recent iPhones provide metric range, but their useful range is short and their returns are sparse. We present DH-Active, a lightweight, training-free geometry back-end that treats the sensor as a metric ruler rather than the sole source of depth. Near-field returns anchor the metric relative pose of two views through PnP; visually trackable samples without a valid depth return are then triangulated under that pose. A parallax/reprojection gate

Listen now | 0:00 Grandpa Bob and author Bob 3:30 Why Bob stopped being an AI doom skeptic 6:59 Can AI solve China’s demographic crisis? 14:09 The irony of China’s open-source AI strategy 17:12 Recursive self-improvement and the singularity 23:33 The Burkean conservative case against AI 38:00 Are we becoming AI meat puppets? 46:32 Google Maps, AI, and the death of interhuman reliance 51:24 Was Pete Hegseth’s military strategy written by a chatbot? 56:45 Trump and Iran: Peace? Wider war? Other? 1

CSET’s Sam Bresnick shared his expert insight in an article published by CNBC. The article examines U.S. AI restrictions and China’s rapid progress in closing the gap with leading American models. The post Trump administration’s AI crackdown opens door for China to close gap appeared first on Center for Security and Emerging Technology .

CSET’s Sam Bresnick shared his expert insight in a newsletter segment published by Politico. The article examines the evolving U.S.–China AI competition, focusing on how cost-efficient Chinese AI models are beginning to gain traction globally and what that could mean for the long-term balance of technological and economic power in artificial intelligence. The post When cheap AI becomes a secret weapon appeared first on Center for Security and Emerging Technology .

On Friday, June 26, EPIC filed an amicus brief alongside civil liberties and criminal defense organizations in New York v. Morris, an important case about cell phone privacy rights during criminal investigations. The brief was filed with the American Civil Liberties Union, the New York Civil Liberties Union, the Legal Aid Society, the Center for … Continued

Announcing: AI StopWatch In our last update, we mentioned we had something new in the works: a dedicated channel for news and analysis about AI. Subscribe to AI StopWatch An experiment from the writers and analysts at MIRI, AI StopWatch posts news and commentary seven days a week. You can read our commentary as it’s […] The post MIRI Newsletter #126 appeared first on Machine Intelligence Research Institute .

The European Commission wants to treat cloud computing as a gatekeeper market. That is the wrong diagnosis, and it would lead to the wrong cure. The Commission’s preliminary view that Amazon Web Services (AWS) and Microsoft Azure should be designated as Digital Markets Act (DMA) gatekeepers for cloud-computing services is more than another skirmish in ... Brussels Goes Gate-Hunting: AWS, Azure, and the DMA’s Cloud Problem The post Brussels Goes Gate-Hunting: AWS, Azure, and the DMA’s Cloud Probl

Washington DC – Today, the Electronic Privacy Information Center (EPIC) announced the addition of four new members to its Advisory Board. Since its establishment, EPIC’s work has been informed by the expertise of leading scholars, experts, and advocates in the fields of privacy, technology policy, and digital rights. We are excited to see the expansion of … Continued

Life sciences has entered an era of computational scale, and for more than a decade, NVIDIA has built the full GPU-accelerated computing stack — spanning hardware, frameworks, libraries, models, microservices and domain-specific tools — to help researchers run more sophisticated workflows and iterate faster. This week, Anthropic announced Claude Science, an AI workbench for science […]

When Jaiveer Singh talks about robots, he doesn’t begin with spectacle. He begins with infrastructure: the boards inside machines, the software that lets developers see through a robot’s cameras and the engineering required before a robot can leave a demo floor to do something useful. As a robotics software engineer who leads the team behind […]

The International Conference on Machine Learning (ICML), held annually for over forty years, is among the most influential conferences in modern AI research. This year in Seoul, ICML is hosting its second workshop on Technical AI Governance Research (TAIGR), and several members of MIRI’s Technical Governance Team (TGT) will attend in July. This post summarizes […] The post Summary: TGT’s 2026 ICML Papers appeared first on Machine Intelligence Research Institute .

At New America, our work spans across five issue areas—each essential to building a nation and a world where everyone can thrive. What Parents of Young Kids Want: Insights from the 2026 National ...

Editor’s note: This post is part of Into the Omniverse, a series focused on how developers, 3D practitioners, and enterprises can transform their workflows using the latest advances in OpenUSD and NVIDIA Omniverse. Vision AI agents are becoming a practical way to automatically turn video data from the physical world into operational intelligence in factories, […]

New OECD analysis reveals how people use GenAI chatbots across countries and demographics, using web traffic data from Similarweb. The post How people are using GenAI chatbots: Evidence from web traffic data appeared first on OECD.AI .

Since the Leveson Report was published in 2012, exposing a collapse in ethical standards across the press, most national newspapers have adopted a similar stance: objection to the very principle of accountability. They believe that while social media, broadcast media and every other industry should be regulated, they alone should be permitted to operate and […]

Google UK shares its latest Economic Impact Report and how to enable more people to unlock the benefits of AI-powered technologies.

Introducing GeneBench-Pro, a new benchmark testing AI performance in genomics, biology, and scientific research using complex, real-world datasets.

A sharply divided U.S. Supreme Court struck a major blow against American consumers on Monday, rewriting constitutional law to bring the Federal Trade Commission and other independent agencies directly under the President’s thumb and threatening their ability to protect the public from harmful business and data practices.

The Supreme Court ruled today that geofence searches violate a reasonable expectation of privacy under the Fourth Amendment.

The Supreme Court ruled today that geofence searches violate a reasonable expectation of privacy under the Fourth Amendment.

The Illinois legislature recently passed House Bill 5511 , which imposes a sweeping, device-level age-gating framework across nearly all internet-enabled hardware, operating systems, and online services. This well-intentioned but deeply flawed piece of legislation will harm young people who rely on the internet to access essential information and find community. That’s why we’re urging the Illinois governor to veto the measure. Under this new regime, digital platforms are forced to collect and s

You have an expectation of privacy in location data that reveals your movements in the physical world, and even short-term surveillance of these movements is a search subject to the Fourth Amendment, the U.S. Supreme Court ruled today in Chatrie v. United States . The case involved geofence warrants, a form of dragnet surveillance police have used to vacuum up location data from electronic devices of people who happen to be in the vicinity of a crime. EFF had joined the American Civil Liberties

Anthropic’s Claude models in Microsoft Foundry — hosted on Microsoft Azure and running on NVIDIA GB300 Blackwell Ultra GPUs — are now generally available, giving Azure-native enterprises a powerful new way to build autonomous and domain-specific AI agents. As agentic AI continues to drive enterprise innovation and becomes more autonomous, organizations need access to computing […]

Today's links Gemini is better than search because Google enshittified search: We're All Trying To Find The Guy Who Did This. Hey look at this: Delights to delectate. Object permanence: Microsoft antitrust overturned; Scammer carves C64; RIP Jim Baen; GOP rep to constituent's child: "drop dead" (literally); CCTVs jacked for botnet; Olympic profitability lie; Human factors in health infosec; Exfiltration via computer fans; Congress's summer schedule: 9 working days; Antitrust is political antigra

These are the key changes | Edition #302

What eras bookend our interregnum?

Showcasing the importance of open source innovation in American AI, Palantir’s new intelligent engine — introduced today — uses NVIDIA Nemotron open models to serve the needs of U.S. government agencies. Open source software has long been a pillar of U.S. technology leadership. In 1969, DARPA connected four university computers — from UCLA, Stanford, UCSB […]

The UK Constitutional Law blog has an article on the recent decision from the Court of Appeal reinstating the proscription of Palestine Action under the Terrorism Act 2000, Secretary of State for the Home Department v R (Huda Ammori) [2026] EWCA Civ 721. The post is concerned with only one of the grounds of the Court […]

Parliament gives final approval to the digital omnibus and a "nudifier" ban, while the Commission rolls out labelling icons and FAQs for the AI-generated content transparency Code.

A new OpenAI report maps how AI could reshape jobs across the EU, highlighting which occupations may face automation, growth, or workflow changes.

Thomas Ahle wants Normal Computing to be the Lovable for chip design: type your intent, and a swarm of agents carries it from design through optimisation, formalisation and verification to tape-out. To get there, his team at wrote their own open-source Verilog simulator, 580,000 lines in 43 days, because commercial EDA verifiers run about $10,000 per core and there are no decent open-source compilers to build on. That sets up the question Tim keeps pressing: if an agent can produce a chip design

One of the most popular posts in this blog’s history was Common Knowledge and Aumann’s Agreement Theorem, based on a lecture that I gave to high-school students 11 years ago. One of the impacts of that post, I’m proud to say, is that (according to Steven Pinker) it helped to inspire Steve’s excellent recent popular […]

Plus: The frontier is already agentic; unlocking innovation; new drugs, food apps without food & Chinese AI job market++

Today's links Zuckerberg's increasingly bizarre war on whistleblowers: Under no circumstances should you rush out and read the book that prompted Mark Zuckerberg to demand $111m and eternal auctorial silence. Hey look at this: Delights to delectate. Object permanence: Flame warriors; Cryptography and casinos; TSA v dying 95 year old woman's adult diaper; Neoliberalism and Brexit; Beyond solutionism; How Thiel cheated with his Roth; Inequality's stabilizer; Palm Pilot school; Gillmor on PR flacks

Over the past two years, the Georgian government has built a comprehensive face recognition enforcement system, procured by a Moscow-based company with ties to the Federal Security Service (FSB). The impact on demonstrators is appalling.

This Pride month, we’re calling on the dating app Grindr to prioritize LGBTQ+ user safety by making privacy the default across its platform. That means no more sharing personal data with advertisers or training AI on private information without users’ opt-in consent. Grindr is a dating app for the LGBTQ+ community; and for queer people, privacy violations can have life-altering consequences. Information that reveals someone’s sexual orientation, gender identity, or HIV status can be used by empl

The most significant factor currently shaping global AI policy is AI's real and projected national security risks. We are entering an AI-driven state of exception | Edition #301

Labs are throwing away the most valuable data.

This is not science fiction. It’s not premature. If towns, cities, states, or the federal government want to act to reign in the emergence of armed police drones and robots , we have precious little time. In the absence of substantial regulation around when and how domestic law enforcement in the United States can deploy force using drones, the companies that markets technology to law enforcement have been moving. It’s past time concerned people take notice. Cities should not procure weaponized

Ignoring EFF’s warnings about the dangers and impossibility of implementing a new mandate for 3D print surveillance software , the California State Assembly has signed off on legislation to do just that. In the process, legislators amended the bill to make it even more confusing, while failing to address the risks to privacy, speech, and consumer rights. We must renew our call on legislators to drop this bill as it heads to the state senate, and protect the tools of creators in the state. Take a

"The current push for AI adoption that we're seeing is directly coming from the financial incentives of AI firms," she added. Because of the massive capital expenditures, the hyperscalers and other AI firms are making a "deliberate push for AI everywhere — no matter whether the demand is there or if customers want it or not." The post Big Tech is spending trillions on AI. Investors now want proof it will pay off. appeared first on AI Now Institute .

We have a new standard policy for releasing frontier AI models. It is not good.

The next war will be won by whoever can manufacture cheap, software-defined unmanned systems faster than the other side can destroy them.

Published on May 15, 2024 5:30 PM GMT This is the third in a sequence of posts taken from my recent report: Why Did Environmentalism Become Partisan? Summary Rising partisanship did not make environmentalism more popular or politically effective. Instead, it saw flat or falling overall public opinion, fewer major legislative achievements, and fluctuating executive actions. Public Opinion One hypothesis is that partisanship was useful, or even necessary, for an issue to become popular. Maybe jour

Plus: Fiery resistance to a nuclear AI data center and A24's Google debacle. Welcome to the first episode of BLOOD IN THE MACHINE: THE SHOW, with the great AI and crypto watchdog, Molly White.

How to get past improvised model licensing

Every society depends on violence workers, but what makes young men take a job that risks their lives and harms others? - by Raúl Zepeda Gil Read on Aeon

Seeking transparency and accountability in Paraguay’s use of facial recognition, EFF, the Association of Technology, Education, Development, Research, Communication (TEDIC), and the Centre for Justice and International Law (CEJIL) filed a complaint with the Inter-American Commission on Human Rights against the state for arbitrarily denying access to information about its implementation and use of the technology as a tool for mass surveillance that erodes people’s privacy rights. The case involve

This week marks four years since Dobbs v. Jackson Women’s Health Organization overturned Roe v. Wade ’s constitutional protections for people seeking abortion care. Anniversaries are a moment to take stock, and over the last four years, EFF has seen firsthand how digital rights and reproductive rights have become increasingly intertwined. One major way this has happened: the fight over abortion has also become a fight over online speech and government censorship as a steady stream of proposed la

Columbia Global Freedom of Expression seeks to contribute to the development of an integrated and progressive jurisprudence and understanding on freedom of expression and information around the world. It maintains an extensive database of international case law. This is its newsletter dealing with recent developments in the field. “I am certain that the machinery of violence […]

The Federal Communications Commission wants to require telecommunications providers to collect vast amounts of personal information from every person who wants a phone number in the name of combatting scam and spam calls. This plan will fail to combat the deluge of unwanted calls people in the United States receive every day while giving untrustworthy companies a gold mine of information that would harm everyday consumer’s privacy, access to communications, and ability to speak freely. The requi

When a car passes an automated license plate reader (ALPR), its plate is captured and instantly compared against a list of vehicles that police are actively looking for or that police have identified for real-time surveillance. These are called “hotlists,” and EFF has learned that one used by agencies across the country targets immigrants on behalf of Immigration and Customs Enforcement (ICE). Agencies using Flock Safety ALPR systems commonly allow the plates their cameras collect to be compared

The post AI Bias Is Putting LGBTQIA+ People at Risk appeared first on Partnership on AI .

On 16 June 2026 Baroness Stowell introduced the Strategic Litigation Against Public Participation Bill (“the Bill”) into the House of Lords. An identical bill has been introduced in the House of Commons by Sir John Whittingdale MP. Unfortunately, despite its title, the Bill is not focussed on the issue of “SLAPPs” or abusive litigation. Rather […]

We've reconstructed the AI economy from the bottom up

A Citizen Lab investigation confirms evidence Russian authorities used Cellebrite tool to hack prominent Russian activist Andrey Pivovarov after the Cellebrite claimed to have ceased sales to Russia. The post Russia used Cellebrite tool to jail activist after company claimed to have ended contract appeared first on Access Now .

Today's links Jailbreaking isn't theft: It wasn't progress when they did it, it's not piracy when we do it back to them. Hey look at this: Delights to delectate. Object permanence: Major AI breakthrough; Disney v Pooh tombstone; Vancouver riot kiss; Farage admits Brexit lies; Protecting the web from its founders; Sanders x Hillary; Surveillance pricing v your dollars. Upcoming appearances: Philadelphia, Chicago, London, Edinburgh, Sydney, Melbourne, Brighton, London, South Bend. Recent appearanc

The Race to Autonomous Driving is Heating Up🔥 A deepdive into Waymo. 🗺️🚘🛣️

Within the next week, Congress is preparing to vote on the KIDS Act , a sprawling package of legislation that seeks to control Americans’ web browsing and private messaging. The package includes a revised version of the Kids Online Safety Act , or KOSA, combined with a collection of other internet bills, study bills, reporting requirements, and new regulations. Instead of debating any of these proposals on their merits, lawmakers are attempting to move them all at once under an ultra-expedited p

A new OpenAI research paper shows how AI agents are transforming work, enabling longer, more complex tasks and expanding productivity across roles.

By Paige Garvin, FPF Communications Intern The Future of Privacy Forum hosted its third annual DC Privacy Forum: Advancing Principled Data Protection, AI, and Digital Governance Practices on June 10th, 2026. This year’s Forum gathered government officials, academics, civil society representatives, and privacy professionals to discuss developments in AI governance, privacy regulation, youth online safety, […]

The post Trump’s Iran War: The Midwife To A Renewable Energy Future appeared first on NOEMA .

Sold to the public as a foreign surveillance tool, Section 702 is the law has let intelligence agencies spy on millions of Americans’ private conversations without a warrant. Despite years of revelations about this law's misuse, Congress has repeatedly reauthorized Section 702 without meaningful reform. Until this month, that is, when it finally lapsed in a major victory for privacy. In our latest EFFector newsletter , we're covering the expiration of Section 702 and what happens next . JOIN OUR

A new report shows how AI systems are already failing LGBTQ+ users. The problem is: it may also the best way to fix moderation issues that traditional systems never managed to address.

While the US debates whether to regulate AI at all, Brazil has built the most detailed AI-and-elections rulebook of any democracy and the gap between the two is becoming a headache for companies

An exclusive survey of AI workers in Kenya reveals how automated management affects their livelihoods. Unions and advocacy groups are beginning to fight back.

At an event for pharmaceutical executives, biotech founders, and researchers on Tuesday, Anthropic announced Claude Science, a major new product intended to support scientific research in the same way that Claude Code supports software engineering. Like Claude Code, Claude Science can autonomously carry out meaningful work when given concise, high-level instructions, and it has access…

OpenAI’s new model broke rules and exploited loopholes more than any model METR has tested to date

"Agentjacking" is the latest demonstration of how easily attackers can exploit an AI agent's inability to differentiate between content and instructions.

We hear concerns that shadow banning is limiting access to health advice for women.

In this "Heard it From a CISO" video, Silverfort CISO John Paul Cunningham explains that AI in cybersecurity workflows is creating opportunities rather than eliminating jobs — and there are more ways than ever to break into this essential field.

The Kurds’ fortunes have ebbed and flowed in recent years, but the fall of the Assad regime in Syria in December 2024, the 2025 decision by the Kurdistan Workers’ Party (PKK) to dissolve and engage in talks with the Turkish government, and the 2026 U.S.-Israeli war with Iran had enormous ripple effects on the lives of Kurds in the Middle East and Kurdish hopes for autonomy. We asked four experts to assess how recent regional events are presenting risks and opportunities for the Kurds in Turkey,

Anthropic released Claude Science, an application that optimizes its large language model for scientists and, especially, those doing research at pharma companies.

Computer scientist Phillip Isola cuts through the hype to explain how AI agents work and what the future might hold for this rapidly advancing technology.

Teams are dealing with a truly dangerous problem — automation that works, but that no one understands.

The World Cup is bringing visitors and AI-driven surveillance systems, but only one of those is certain to leave when the games are done.

Artificial intelligence is transforming what is possible in agriculture, but industry leaders should be wary of investing in AI without first laying the groundwork. The use cases are promising, especially for an industry navigating volatile fertilizer costs, unpredictable weather, and margins that leave little room for error. Research shows AI-enabled predictive models can improve crop…

“If you can work in the Pacific, you can work anywhere in the world,” said Maj. Gen. Gavin Gardner.

If you’ve seen reports of a wildfire in your region and you’re looking for open source data, NASA’s fire-tracking tool is often the first place to start. It provides a heat signature and an approximate location. But detection is only the first step in understanding what’s happened. In this guide, we explore ways to analyse […] The post Burning Forests: Tools for Tracking and Reporting Wildfire Damage appeared first on bellingcat .

The extent of China’s drone dominance — and how to decouple from it — has long been a source of debate and anxiety in Washington. Last month, the Wall Street Journal reignited controversy by publishing a visual analysis of military quadcopter components, exploring China’s advantages in parts manufacturing and cost. The director of the Defense Innovation Unit objected to the report, stating on X that “By leaving out the dozens of U.S. companies that have plunged into drone component manufacturing

Il Green Deal europeo è davvero in crisi? No, se guardiamo agli investimenti sulla transizione energetica. Capiamo che periodo stiamo attraversando e cosa ci attende per il prossimo futuro

Chinese autonomous driving company Momenta launched its Hong Kong public offering on June 29, with plans to list on the Hong Kong Stock Exchange’s main board under the ticker 6880.HK. The company is offering 19.94 million Class A ordinary shares at HK$295.60 each, aiming to raise about HK$5.89 billion ($751 million) before any over-allotment option […]

Reducing compensation to symbolic amounts strips the regulation of its primary purpose: consumer protection and accountability.

The legislation cleared the House despite opposition from some kids’ safety advocates and resistance from senators backing a competing proposal.

The infostealer was delivered via CVE-2026-48558, a critical authentication bypass vulnerability in SimpleHelp, targeting credentials linking development and admin environments to wider enterprise systems.

The genomic generation is on its way

Does life feel Orwellian sometimes? One researcher has a solution for you: graphic tees that confuse the neural networks in surveillance cameras.

Associate Professor Anna Huang delivers the keynote address, “In Search of Human-AI Resonance,” to a capacity crowd.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Imagine coming in to work to learn that a new underling will report to you. The worker is not a person but an AI tool—one that your company nonetheless calls Alex, an…

Tension over digital regulation clouds ongoing talks to launch a new EU-U.S. tech "dialog."

OpenAI’s latest model, GPT-5.6, is waiting for government approval.

Germany has most jobs at risk while Luxembourg has largest share in occupations that may actually grow with AI, firm says in new report.

Today, you probably asked a question of a large language model, or accepted a connection suggestion on LinkedIn, or watched a recommended video on YouTube, or took a different route to work based on a traffic prediction from Google Maps. In other words, you probably used artificial intelligence. But what you might not know is how much energy that interaction consumed or why. AI requires processing massive amounts of data, which is usually done in large data centers populated by thousands of GPUs

Beatbot Sora 70 si prende cura in modo preciso e totale della pulizia dell'acqua dalla superficie alle profondità

The requests inject claims of political bias into a process that will determine if Disney keeps its lucrative licenses.

Portugal's National Health Service signed a deal with digital health company Sword for its AI-assisted virtual physical therapy care.

As the relationship between the United States and Canada continues to degrade, it now comes at the expense of each country’s industrial security.Last month, the Pentagon announced the unilateral suspension of the 86-year-old Canadian Permanent Joint Board on Defense in response to what the White House sees as Ottawa’s failure to present a credible plan to spend 3.5 percent of GDP on defense by 2035. And the opening of the gleaming new bridge between Detroit and Windsor has been long delayed in t

During the Cold War, Europe kept asking whether Washington would risk an American city to save a European one. It was an impolite question, but a useful one, which is why it never quite left the room. It has now packed its bags and moved east. Earlier this year, French President Emmanuel Macron created quite a stir with an important speech on French nuclear weapons policy. Under what he called a new path of dissuasion avancée, or “forward deterrence,” he declared that just as French strategic su

El Instituto de Investigación Biomédica de Málaga busca personas voluntarias de entre 18 y 65 años para probar Pandora, una intervención digital personalizada que han desarrollado investigadores de España y Chile. El objetivo de la aplicación y el proyecto es mejorar el bienestar emocional, mental y físico

Per il fondatore di Morning Brew i lettori oggi cercano anzitutto una voce in cui riconoscersi. “La sovranità? Si ottiene investendo sui giornalisti”. Nella sua nuova iniziativa imprenditoriale, centrata sull'AI, monetizza con consulenze e formazione

Edison Scientific and investment firm Population Health Partners are teaming up to leverage AI agents in drug discovery and development.

RealT prometteva di aprire il mercato ai piccoli investitori, ma il progetto si è presto scontrato con immobili degradati, promesse disattese e problemi legali

On June 28, the International Society for Transforming Education — the organization behind the editorially independent news site EdSurge — released an expanded version of its “ Profile of an AI-Ready Graduate ,” a framework designed to help K-12 educators teach students how to work with artificial intelligence. The updated framework, designed with support from the nonprofit Britebound, goes beyond basic literacy to higher-order skills. It identifies six roles the organization says students shoul

Worried that their children may not apply to the right schools, parents are enlisting application consultants for help — and getting scammed.

But concerns persist about the power and governance of software agents.

The education nonprofit drops its combined acronym for a unified brand.

This article is the result of a collaboration with The Sunday Times. You can find their corresponding piece here. Every Friday evening, the brochure says, players can compete to win cash prizes in one of the world’s fastest-growing racquet sports. The padel club in Dubai’s west is the picture of modern wellness culture: climate-controlled courts, […] The post Poster Boy: Sanctioned Kinahan Cartel Lieutenant Found Playing Padel in Dubai appeared first on bellingcat .

The advance of these technologies is not occurring as an exception. They are established silently, without public debate, without transparency and without people knowing the fate of their data

The revised bipartisan package is scheduled to be considered on the House floor next week under suspension of the rules.

Rural health system Reid Health is using Abridge’s AI-powered documentation tech to reduce burnout among nurses. It appears to be working — after-shift charting is down by up to 45 minutes, and the RN vacancy rate has been cut more than in half, said Misti Foust-Cofield, Reid’s chief nursing officer. The post Documentation Tools Aren’t Just for Doctors: Inside Reid Health’s Deployment of Abridge’s Nurse Tech appeared first on MedCity News .

Children’s online safety groups are pressing House lawmakers to oppose the bipartisan measure, arguing that it weakens safeguards.

Transformer Weekly: AI companies’ talent problem, KOSA developments, and Google’s new AI policy framework

Nuclear policy debates are at their best when they force hard questions about risk, deterrence, and military necessity. They are at their worst when disagreement is recast as bad faith. In 2018, as an outgrowth of a rigorous policy review process, the Trump administration’s Nuclear Posture Review identified a need for supplemental low-yield nuclear capabilities to augment the U.S. nuclear arsenal. This was presented as an effort to raise the nuclear threshold of adversaries who may believe they

To help robots do chores in places like homes and factories, a new approach from MIT uses one language model to clarify users’ instructions, then another to ignore irrelevant info.

Should you base the whole economy on companies that hemorrhage money?

To understand how close Myanmar’s pro-democracy resistance came to victory last year — and how far it has slipped since — there is no sharper microcosm than the story of a Gen Z sniper. In April 2025, a female teenager, Anina, enraptured the world, her youth-driven “Spring Revolution” a vivid foil to the sclerotic forces of military dictator Min Aung Hlaing (officially called the Tatmadaw). When her unit captured the town of Falam, fortune seemed on the resistance’s side. Yet, a year later, regi

Beijing Mercedes-Benz Sales & Service Co. plans to reduce its workforce from about 900 to under 600 through two rounds of adjustments, with roughly 10% of the process already completed, according to sources. The move is not an isolated case. Since 2025, Mercedes-Benz’s operations in China have carried out personnel optimization across multiple business units, […]

One of the biggest crowd-pullers at MWC Shanghai 2026 was a fully autonomous humanoid robot penalty shootout, rather than a smartphone launch or an AI keynote. Held over two days at the Shanghai New International Expo Centre, the competition drew more than 10,000 spectators as eight Chinese embodied AI teams battled through nearly 100 rounds […]

While studying degraded grasslands in northern China, one scientist started keeping chickens — and discovered a secret weapon against desertification.

Biotech executives reveal concerns over Chinese biotech, the profitability of AI, and the durability of Trump's drug price moves.

The tools are explicitly intended for generating deepfake nudes of a former Trump cabinet official, sitting members of Congress and a top American judge, a Transformer investigation found

The MAHA movement has been promoting the plant-based substance ibogaine as a remedy for opioid addiction, despite the lack of clinical trials on its safety and efficacy. One physician who practices addiction medicine discusses the risks of ibogaine and argues for the need to support proven treatments.

Leading the Future spent big to stop the author of the state’s AI safety law from going to Congress. Then came the backlash.

A new bipartisan group will work with corporate donors like Anthropic, OpenAI, Amazon, Microsoft and Bank of America to retrain workers displaced by the AI boom.

The IEC is gearing up to deliver a more technology-enabled process at the polls later this year.

A new system, known as Murakkab, optimizes the design and deployment of multistep workflows that power AI applications.

In 2024, Paul Scharre and Michael Depp wrote, “Artificial Intelligence and Nuclear Stability,” where they argued integrating artificial intelligence into the nuclear chain of command presents both opportunities and risks. Two years later, as AI becomes increasingly integrated into military systems and processes, we asked them to revisit their arguments. Image: Senior Airman Jason Wiese via Wikimedia CommonsIn 2024, you argued that integrating artificial intelligence into the nuclear chain of com

In this edition of STAT's AI Prognosis: Brittany Trang brings the latest from BIO on how biotech companies are approaching artificial intelligence.

A new study published in Nature uses artificial intelligence to identify people at high risk for sudden cardiac death, and pinpoints a possible reason.

Warning: Includes graphic descriptions of animal harm and images of animal parts from the outset. A Bellingcat investigation has uncovered a Myanmar-based wildlife trafficker who has operated openly across social media for at least six years, claiming to have sold tiger bones, rhino horn, elephant skin and other products from protected and endangered species to […] The post Rhino Horn, Leopard Skin and Tiger Claws Sold Openly on Facebook appeared first on bellingcat .

I was listening to Ezra Klein interviewing a left-wing Democratic Party strategist the other day about what a post-Trump U.S. foreign policy might look like, and it was a pretty striking demonstration of Europe’s irrelevance right now that the only Western European country mentioned in the 90 minutes of the chat was the UK, and The post How governments enable kleptocrats by doing nothing appeared first on Coda Story .

The NY-12 House race drew over $27m from various AI PACs. But it’s hard to unpick their impact.

This exclusive Cogs of War interview is with Maxwell Maduka, the co-founder and chief engineer of Terra Industries, an African defense technology company building autonomous drone and counter-drone systems designed for the continent’s operating conditions. As cheap imported airframes flood African markets and non-state actors employ drones across the Sahel, we asked Max why Terra is betting on Africa building its own defense industrial base.Cheap Turkish and Chinese drones and sensor systems hav

Armies run on more than what happens at the front. Behind every operation is a vast amount of coordination, administration, logistics, and judgment. Bill Pessin, senior vice president of national security at Salesforce and a former U.S. Army logistics officer, joins Jonathan to discuss how military organizations can use AI agents, what makes these tools different from ordinary software, and why safety and accountability matter when new technology enters national security work. They also discuss

At least two new start-up companies, Preventive and Origin Genomics, say they are developing strategies that combine gene editing with in vitro fertilization to correct disease-causing mutations. Advocacy groups for people living with these genetic disorders have been quiet on the developments.

How to apply effective governance to harness the benefits of A.I. and mitigate its risks ...

Call for Tenders: European Data Market Study 2027–2028 dumimar Tue, 06/30/2026 - 14:43   Opening: 29 June 2026   Closing: 12 August 2026 This call for tenders will fund the European Data Market Study 2027–2028, which will provide quantitative evidence on the EU data market and data economy at large. AdobeStock © Miha Creative The study will build on data collected since 2014 and support evidence-based policymaking by monitoring key trends, measuring progress towards EU digital policy o

(box) (popup) Human Rights at local and regional levels Human rights and the environment The Case Law of the European Court of Human Rights - Local and Regional Authorities Children's Rights LGBTI ...

BY THE PRESIDENT OF THE UNITED STATES OF AMERICA A PROCLAMATION 1. Fertilizers are an essential component of agriculture and food production. Producers of corn, soybeans, wheat, and a variety of other crops need phosphate fertilizers to ensure strong crop yields to feed the population. Food production is critical to human health, farm security, and to […] The post Declaration of Emergency and Authorization for Temporary Duty Free Importation of Phosphate Fertilizer Morocco appeared first on The

MEMORANDUM FOR THE ADMINISTRATOR OF THE ENVIRONMENTAL PROTECTION AGENCY By the authority vested in me as President by the Constitution and the laws of the United States of America, I hereby direct: Section 1. Purpose. During the previous administration, crushing environmental regulatory burdens caused the average cost of vehicles to soar. My Administration has therefore […] The post Lowering the Cost of Living by Promoting the Freedom to Fix appeared first on The White House .

The 5th edition of the ECML-EC Summer Academy took place from 29 June to 3 July 2026 in Graz, ...

The first, and the prerequisite for the others, strengthens the institutional foundation through enacting the Construction Industry Development Bill (CIDB) (formally called Uganda Construction ...

This article was originally published by IAPP linked here. Five Eyes highlights rising AI cyber risk, as the author explores practical legal, compliance and business steps organizations can take to strengthen resilience. Last week, the cybersecurity agencies of Five Eyes released a statement, “The AI shift in cyber risk: Why leaders must act now.” For [...] The post Thought for the week: Five Eyes call to action for business leaders on AI-driven cyber risk appeared first on Connect On Tech .

The European Social Charter is a treaty of the Council of Europe that guarantees fundamental social and economic rights. It complements the European Convention on Human Rights, which refers to civil ...

Enhanced transparency and exchange of information to put an end to bank secrecy and fight tax evasion and avoidance ...

In brief Organizations that develop or deploy AI agents – autonomous systems that can pursue goals and take actions with limited human intervention – are navigating a rapidly evolving US legal landscape that pulls agentic AI under laws that govern action. Emerging legal developments support the view that accountability generally runs to the humans and [...] The post US Legal Accountability for AI Agents: When AI agents act, who is responsible under US laws? appeared first on Connect On Tech .

The U.S. Nuclear Regulatory Commission (NRC) is proposing to revise its regulations to modernize security and fitness-for-duty requirements to enhance efficiency, consistent with Executive Order 14300, "Ordering the Reform of the Nuclear Regulatory Commission." The proposed revisions are intended to reduce regulatory burden, where appropriate, while continuing to provide reasonable assurance that safety and security will be adequately maintained at NRC-licensed facilities.

By the authority vested in me as President by the Constitution and the laws of the United States of America, it is hereby ordered: Section 1. Purpose and Policy. Executive Order 14212 of February 13, 2025 (Establishing the President’s Make America Healthy Again Commission) established the Make America Healthy Again (MAHA) Commission, with an initial […] The post Advancing Regenerative Agriculture and Strengthening American Farm Resilience appeared first on The White House .

Tuesday From 2.30pm Questions to government adequacy of legal protections for ancient trees discussions with international partners regarding global governance frameworks for artificial intelligence ...

In letters published today, the Council of Europe Commissioner for Human Rights, Michael O’Flaherty, asks ministers responsible for migration policy in Austria, Denmark, Germany, Greece and the ...

AIID editor's note: Please see the original source for the full report and all of its findings. Over the past year, a team of GPTZero investigators has used our Hallucination Check tool to uncover hallucinated citations in government repor ... (https://incidentdatabase.ai/cite/1563#7481)

The Navy stopped a maritime drone test early and urgently requested support from the Coast Guard and local harbor patrol agents to help rescue a participating tugboat captain from waters off the California coast last week, multiple sources ... (https://incidentdatabase.ai/cite/1561#7459)

AI-powered software has allowed gas station operators across California to illegally collude and drive up prices at the pump, according to a federal lawsuit. The proposed class action lawsuit, filed Monday, accuses gas station giants inclu ... (https://incidentdatabase.ai/cite/1559#7460)

Artificial intelligence is costing motorists in California more money at the gas pump, according to a new lawsuit. The lawsuit claims companies in the state are using data collected by AI to manipulate gas prices. Companies mentioned in th ... (https://incidentdatabase.ai/cite/1559#7461)

Gasoline prices have soared nationwide since the U.S. launched a war on Iran, but they remain far higher in California than other states, a dollar or more per gallon. And one reason they're not falling, according to a newly filed lawsuit, i ... (https://incidentdatabase.ai/cite/1559#7462)

SACRAMENTO, Calif. (CN) --- Three Californians filed a class action over what they call an artificial intelligence-based pricing system that has wrung more money out of drivers in a time of explosive gas prices. The plaintiffs sued Knowled ... (https://incidentdatabase.ai/cite/1559#7463)

The proposed class argues that California's fuel prices are high due to an illegal algorithmic price-fixing scheme orchestrated by an algorithmic pricing company and the largest fuel retailers. AI software used by gas stations across Calif ... (https://incidentdatabase.ai/cite/1559#7464)

Topline A new lawsuit accuses a slew of gas station owners in California, including Walmart, Speedway and Albertsons, of using an AI tool developed by a company called Kalibrate to artificially inflate prices at the pump---causing prices t ... (https://incidentdatabase.ai/cite/1559#7465)

California drivers have filed a proposed class action against BP, Marathon, Walmart, and other major gas station operators, alleging they used an artificial intelligence pricing tool to fix pump prices across the state, according to Reuters ... (https://incidentdatabase.ai/cite/1559#7466)

Gas station operators in California are being accused of colluding to keep fuel prices artificially high. The alleged culprit? A piece of software that uses AI to collect and compare non-public pricing and sales figures from participating s ... (https://incidentdatabase.ai/cite/1559#7467)

Californians pay the highest gas prices in the U.S., and a proposed class action says that the issue has been exacerbated by an AI tool that smartly squeezes customers for the best profits. A newly filed lawsuit at the Sacramento, ​Californ ... (https://incidentdatabase.ai/cite/1559#7468)

Dive Brief: Several California residents have sued 7-Eleven, Circle K, BP and other retailers for allegedly using an AI-powered fuel pricing algorithm to increase gas prices in the state, according to a lawsuit filed in the U.S. District C ... (https://incidentdatabase.ai/cite/1559#7469)

SACRAMENTO, Calif. --- Gas station operators including bp, Circle K, Marathon Petroleum, 7-Eleven, Walmart and Albertsons face a proposed class action lawsuit from California drivers accusing them of using AI to boost fuel prices. Accordin ... (https://incidentdatabase.ai/cite/1559#7470)

AIID editor's note: Please visit the original source for the full article. A group of California consumers filed a proposed class-action lawsuit alleging that gas station operators including Walmart, Marathon Petroleum, BP and 7-Eleven use ... (https://incidentdatabase.ai/cite/1559#7471)

AIID editor's note: Please visit the original source for the full article. By Bryan Koenig (June 22, 2026, 7:22 PM EDT) -- Consumers sought Monday to widen the campaign against alleged algorithmic price fixing, in a proposed class action a ... (https://incidentdatabase.ai/cite/1559#7472)

AIID editor's note: Please visit the original source for the full article. A group of California motorists has filed a proposed class-action lawsuit alleging that several major fuel retailers and an energy pricing software provider used ar ... (https://incidentdatabase.ai/cite/1559#7473)

Three California residents filed a federal class action lawsuit on June 22, 2026, in the U.S. District Court for the Eastern District of California against Knowledge Support Systems, d/b/a Kalibrate, along with 14 of the largest gas station ... (https://incidentdatabase.ai/cite/1559#7474)

Gas station operators across California used an AI-powered software system to illegally coordinate pricing and drive up fuel costs, according to a federal lawsuit filed Monday. Driving the news: The proposed class-action suit accuses major ... (https://incidentdatabase.ai/cite/1559#7475)

Three California residents are suing a fuel pricing company and several gas station operators, alleging that they use artificial intelligence-based pricing systems to raise gasoline prices in an uncompetitive manner. "Californians are bei ... (https://incidentdatabase.ai/cite/1559#7476)

California is famous for plenty of things, but few grate quite like its fuel prices, which already rank among the steepest in the nation. As of today, regular runs $5.56, mid-grade $5.788, and premium $5.95. Now a group of residents claims ... (https://incidentdatabase.ai/cite/1559#7477)

A class-action filed in Sacramento federal court names BP, Marathon, 7-Eleven, Walmart and Albertsons, alleging their shared use of Kalibrate Fuel Systems' pricing algorithm inflated California gas prices by as much as 22 cents a gallon. C ... (https://incidentdatabase.ai/cite/1559#7478)

A new federal lawsuit alleges that gas station companies across California are engaged in an illegal conspiracy, powered by AI software, to raise prices. The class action lawsuit claims the corporate owners of over 1,700 California gas sta ... (https://incidentdatabase.ai/cite/1559#7479)

On the heels of filing a landmark federal lawsuit against social media and tech giants, Bucks County District Attorney Joe Khan today announced the arrest of a New Britain Borough man facing multiple felony charges for producing and possess ... (https://incidentdatabase.ai/cite/1562#7480)

April 21 (Reuters) - Sullivan & Cromwell, a premier Wall Street law firm, apologized to a federal ‌judge for submitting a court filing with inaccurate citations and other errors generated by artificial intelligence. In a letter dated April ... (https://incidentdatabase.ai/cite/1558#7456)

June 22 (Reuters) - Gas station operators ‌including BP (BP.L), opens new tab, Circle K (ATD.TO), opens new tab, Marathon Petroleum (MPC.N), opens new tab, 7-Eleven (3382.T), opens new tab, Walmart (WMT.O), opens new tab and Albertsons (ACI ... (https://incidentdatabase.ai/cite/1559#7457)

ضت محكمة جنايات دمنهور، الدائرة السابعة، برئاسة المستشار الدكتور سامح عبد الله، وعضوية المستشارين أحمد خضر، وأحمد خليل، ومصطفى رفاعي، وسكرتارية خالد يوسف، بحبس شاب لمدة سنة مع الشغل وإيقاف تنفيذ العقوبة، ومحو كل الصور والرسائل المتعلقة بالج ... (https://incidentdatabase.ai/cite/1560#7458)

LONDON, June 5 (Reuters) - Last Christmas Eve, NewsBreak, opens new tab, a free app with roots in China that is the most downloaded news app in the United States, published an alarming piece about a small town shooting. It was headlined "Ch ... (https://incidentdatabase.ai/cite/1554#7451)

A partner at Boies Schiller Flexner is seeking to file a corrected brief in litigation against the Church of Scientology after taking responsibility for "material citation errors" that it contained. In a Sept. 19 declaration, partner John ... (https://incidentdatabase.ai/cite/1555#7453)

An X user managed to trick AI chatbot Grok into sending around $200,000 worth of crypto after exploiting its link with an automated trading bot. The incident involved Grok and 'Bankrbot', two AI systems with wallet access, which were manip ... (https://incidentdatabase.ai/cite/1556#7454)

Queens District Attorney Melinda Katz announced that Jonathan Rinaldi has been charged with forgery and criminal possession of a forged instrument for allegedly creating and distributing false political endorsements and fake news articles u ... (https://incidentdatabase.ai/cite/1557#7455)