AI ethics on Thursday, 30 July 2026

First-in-nation law sets up test on states’ power to regulate use of AI as it tries to outlaw fake nude images of real people Elon Musk’s company xAI has sued Minnesota over the state’s first-in-the-nation law banning “nudification” technology on websites and apps, potentially providing a test for how far states can go in constitutionally regulating the use of artificial intelligence. Musk’s company sued on Monday in federal court, days before the law is set to take effect on Saturday and make M

OpenAI-Modelle attackierten vergangene Woche scheinbar weitere Software. Die Firma erklärt nun ausführlicher, was genau passiert ist.

According to CCTV News, the National Energy Administration (NEA) held a press conference on Wednesday, announcing that China’s renewable energy sector saw rapid growth in the first half of 2026, with renewable power generation accounting for more than 40% of total electricity generation for the first time. During the first half of the year, China’s […]

A majority of surveyed global asset management firms plan to raise their artificial intelligence budgets by at least 50 per cent within the next year as the technology transforms the finance industry, a recent study showed. The adoption of AI is also going to have a big impact on labour-intensive operations: 62 per cent of polled fund managers expect transformative change in data generation and summarisation, according to a study released Tuesday by US fintech firm Clearwater...

Bond markets are increasingly dominated by a bet on the same thesis as other asset classes

Operators blame planning rules and low prices for a lack of investment

The co-founder of the UK-based science start-up explains how AI can help create new materials to address some of the world’s most complex challenges

Start-up Ahbstra applies MOF technology to draw moisture from the atmosphere at volume, using minimal energy

Join experts at MediaNama event to discuss age verification, social media restrictions for children, platform design, privacy, online safety, and practical regulatory approaches. The post Final Call: Age Verification and Restricting Social Media for Children, Delhi, 31 July #NAMA appeared first on MEDIANAMA .

Hedge fund has held talks with existing investors and lenders in recent days

Author Joyce Carol Oates came to the defense of Christopher Nolan’s “The Odyssey” after translator Emily Wilson wrote a viral review attacking the film. “Rather than disagreeing with interpretations of Homer in a collegial manner, this person, who has benefited enormously from Nolan’s film, speaks in the crude language of MAGA folks attacking someone with […]

China's Commerce Ministry said Thursday the U.S. Federal Communications Commission has repeatedly ignored Beijing's restrained stance.

For investors anxious to hear more about Meta's plans to make money from its big AI spending, Mark Zuckerberg said there's a trade-off.

Christie's auction house handled the One Goal auction, which benefits the FIFA Global Citizen Education Fund, a $100 million initiative to expand access to quality education and football for children worldwide.

The ranking member of the House Committee on Energy is demanding SpaceX records and a tour of its data centers and power plants in and around Memphis.

Freehand, a startup building AI agents that manage supply-chain spend for giants including Meta and Pfizer, has raised $75 million in funding.

Publication date: October 2026 Source: Telecommunications Policy, Volume 50, Issue 9 Author(s): Shuyu Pan, Ye Yuan, Wenle Jiang, Zixin Xu, Zhelun Zhu, Jiacheng Liu

Publication date: September 2026 Source: Technology in Society, Volume 88 Author(s): Marina Raguž, Domagoj Dlaka, Marko Švaco, Petar Marčinković, Dominik Romić, Filip Šuligoj, Bojan Šekoranja, Darko Chudy, Bojan Jerbić

Publication date: September 2026 Source: Technology in Society, Volume 88 Author(s): Mohd Shuaib, Mohammad Haseeb, Fei Fan

Publication date: September 2026 Source: Technology in Society, Volume 88 Author(s): Ye Zheng, Muhammad Farhan

Publication date: Available online 28 July 2026 Source: Computers and Education: Artificial Intelligence Author(s): Ningxia Wang, Di Zou, Haoran Xie, S.Joe Qin

arXiv:2607.26062v1 Announce Type: new Abstract: Background: This work investigates the presence of implicit bias in Large Language Model (LLM)-based chat AI models directed toward people with intellectual disabilities (ID). Objective: The study aims to identify and measure representational differences related to people with ID and examine them to identify implicit biases inherent in AI chat generation technologies. Methods: Utilizing the GPT-4-Turbo model, we requested story-generation based on

arXiv:2607.26063v1 Announce Type: new Abstract: Personalised learning systems often assume that mathematical ability is combined of discrete abilities, acquired sequentially and dependent upon first acquiring foundational abilities, and students often report different strengths. In this work, we explore the validity of these assumptions by applying clustering methods to a large dataset of 119,034 students, spanning 13 national-level exams sat in the United Kingdom and collected by the platform.

arXiv:2607.26064v1 Announce Type: new Abstract: AI systems are becoming autonomous research agents that generate hypotheses, design experiments, and produce discoveries at scales beyond human oversight. As seen by increased submissions to ML venues, the verification gap between scientific output and our ability to check it is already widening, and autonomous agents make it worse by magnitudes given human-agent asymmetry. We argue that science must evolve its verification infrastructure, as it ha

arXiv:2607.26067v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for estimating item difficulty in educational assessment. However, it remains unclear whether such estimates reflect how learners actually experience difficulty. This study investigates the alignment between LLM-generated difficulty ratings and empirical student performance on basic mathematics tasks. Four widely used LLM-based systems generated difficulty ratings on a 1-100 scale for 32 arithmetic

arXiv:2607.26069v1 Announce Type: new Abstract: As AI systems are rapidly integrated into critical economic, governmental, and national security functions, the gap between AI adoption and AI security readiness continues to widen. This paper presents a prioritized agenda for advancing AI security, informed by structured interviews with leaders across industry, government, and civil society, and refined through a multi-sector expert workshop. Participants identified and ranked the highest-importan

arXiv:2607.26317v1 Announce Type: new Abstract: Psychometric calibration for educational tests typically requires costly human response data. Large language models (LLMs) simulated examinees offer a promising route to early calibration, but their responses are too accurate and too uniform. We propose Cognitive Diagnostic Profiling (CDP), a zero-shot framework that prompts LLMs to simulate plausible examinees with diverse cognitive profiles: binary attribute-mastery patterns are rendered as natur

arXiv:2607.26387v1 Announce Type: new Abstract: Collaborative knowledge work is changing in ways that go beyond disclosure or transparency. LLM agents are now embedded in how teams research, design, write, and decide: mediating between members, synthesizing inputs, reformulating ideas, and drafting shared outputs. They do not only facilitate collaboration; they operate within the workflow at the moment contributions are being formed. In doing so, they risk undermining the social conditions under

arXiv:2607.27029v1 Announce Type: new Abstract: This paper reports findings from a structured participatory foresight study comprising two expert forecasting studios convened by The GovLab between 2025 and 2026. The studios brought together nineteen senior practitioners spanning official statistics, digital and trade policy, open science, AI governance, geospatial systems, and public-sector innovation across multiple jurisdictions. Applying a qualitative signal-scanning methodology grounded in t

arXiv:2607.26068v1 Announce Type: cross Abstract: Existing AI governance frameworks, including the EU AI Act and NIST AI RMF, address safety, transparency, and accountability but do not operationalize quantitative constraints on macro-socioeconomic stability. As a result, AI systems may satisfy regulatory requirements while contributing to labor displacement, rising inequality, and reduced economic resilience. We introduce the Human Utility Factor (HUF), a differentiable welfare metric that mode

arXiv:2607.26121v1 Announce Type: cross Abstract: Embodied intelligence integrates learned perception and decision making with real-time computation, control, and physical interaction. Because failures can cause immediate physical or operational harm, task completion alone does not establish trustworthiness. We define trustworthy embodied intelligence as the sustained capacity to execute specified tasks reliably under environmental and system variation while maintaining risk within acceptable bo

arXiv:2607.26204v1 Announce Type: cross Abstract: A decentralized autonomous organization (DAO) is a governance entity that allows its stakeholders to manage blockchain-based protocols through smart contracts. The DAO explicitly specifies how stakeholders make and enforce decisions concerning a protocol's operation in a smart contract, aptly referred to as its governance contract. The design of this governance contract, therefore, has far-reaching implications for the security (trust) and privac

arXiv:2607.26313v1 Announce Type: cross Abstract: Agentic systems act, so a defect in the evidence they retrieve becomes a wrong action with a currency cost. The most dangerous enterprise defects are metadata-borne: a stale price or a superseded record, perfectly well-formed in the payload and betrayed only by freshness, lineage, or provenance. Such a defect never enters the agent's context, and an agent cannot doubt data it cannot see. On a priced replenishment benchmark, a competent agent sile

arXiv:2607.26348v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as synthetic users, stand-ins for human respondents whose simulated answers feed product, policy, and market decisions. We ask when this substitution is valid and when it fails, and package the answer as an evaluation framework for intelligent synthetic-user systems. A single protocol, run across four models spanning two families and an 8B-to-frontier capability range, is applied to two independe

arXiv:2607.26654v1 Announce Type: cross Abstract: Post-training alignment is often shallow, eroding under fine-tuning. Whether midtraining interventions, cleanly isolated from post-training, can produce durable alignment remains untested. We test this via constitutional midtraining: inserting principled, values-based content into midtraining against a replay-only control at 120B scale. Our 394M-token constitutional corpus, built from Anthropic's Constitution, uses a 2x2 factorial design (curricu

arXiv:2607.26886v1 Announce Type: cross Abstract: When asked to describe a medical image that was never attached, frontier vision-language models do not abstain: they confabulate a diagnosis. We show that this confabulation is not random. It is structured by who the patient is said to be. Across chest X-ray, brain MRI, and dermatology, Claude Opus-4.7, GPT-5.4, and Gemini-3.1-Pro are each queried with only a demographic descriptor and no image, and changing the descriptor systematically shifts t

arXiv:2607.27191v1 Announce Type: cross Abstract: Forecasts of explosive AI progress hinge on AI agents automating AI research. But evidence on whether agents can carry out open-ended AI research is thin. Current evaluations either test agents on narrow, verifiable tasks, which excludes open-ended research, or submit AI-generated papers to blind peer review, which is overstretched, stochastic, and suffers from poor review quality. We introduce a third way to measure progress towards AI R\&D auto

arXiv:2604.24155v4 Announce Type: replace Abstract: The project of aligning machine behavior with human values raises a basic problem: whose moral expectations should guide AI decision-making? Much alignment research assumes that the appropriate benchmark is how humans themselves would act in a given situation. Studies of agent-type value forks challenge this assumption by showing that people do not always judge humans and AI systems identically. This paper extends that challenge by examining tw

arXiv:2502.10605v4 Announce Type: replace-cross Abstract: Problem definition: Estimating causal effects of interventions is central to policy and operations, but outcome data are often missing or costly to obtain. LLMs can provide text annotation at scale but may be subject to unknown bias. When ground-truth outcomes require expensive expert labeling or follow-up, budget limits typically allow only a fraction of the data to be labeled. Motivated by collaboration with a nonprofit conducting stree

arXiv:2503.10647v2 Announce Type: replace-cross Abstract: This study evaluated the diagnostic reliability of two Large Language Models (LLMs), Google Gemini 2.0 Flash and OpenAI ChatGPT-4o, across three dimensions: consistency under rephrased inputs, susceptibility to irrelevant prompt content, and responsiveness to added clinical context. We designed 52 clinical scenarios and modified each under controlled conditions. For consistency, scenarios were rephrased with demographic, wording, and exam

arXiv:2507.04398v3 Announce Type: replace-cross Abstract: Generative AI is becoming part of academic writing, but its educational value depends on how control is shared between learner and system. This study examined an agency gap: performance differences that may arise when AI agent initiative is misaligned with learners' generative AI literacy. Seventy-nine medical and nursing students completed two multimodal analytical writing tasks using healthcare simulation data visualisations. They were

arXiv:2512.18072v3 Announce Type: replace-cross Abstract: Conversation is a cornerstone of social connection and is linked to well-being outcomes. Conversations vary widely in type with some portion generating complex, dynamic stories. One approach to studying how conversations unfold in time is through statistical patterns such as Heaps' law, which holds that vocabulary size scales with document length. Little work on Heaps' law has looked at conversation and considered how language features im

Collaborative robots (cobots) are increasingly deployed in industrial as well as non-industrial domains to support human-centered operation. While physical safety and task efficiency have received considerable attention, less is known about how feedback modality influences operator experience and physiological responses under different collaboration demands. This study examines the effects of feedback modalities in two human–cobot collaboration scenarios representing distinct coordination struct

Embodied picking for e-commerce fulfillment remains vulnerable to dense clutter, reflective packaging, and background variation, which can undermine the effectiveness and robustness of visuomotor policies learned from demonstrations. A key limitation is the absence of explicit object grounding, causing policies to exploit spurious contextual cues rather than task-relevant visual evidence. To address this issue, we propose the Foveated Diffusion Policy (FDP), which integrates object-centric visua