News & Research
The latest AI research and news with real-world stakes — each item sourced, dated, summarized in plain English, and tagged by impact area. Every item is checked against its source before it appears.
Research
Thomson: Continual Learning of Frontier Models for SovereignAI
Shengzhuang Chen, Jerrod Parker, Yejin Bang et al.
arXiv (Cornell University) · 2026-08-27
This paper presents Thomson, a frontier AI model built using Continual Learning on open-weight models to demonstrate that high-performing AI can be developed by a broader range of institutions, not just heavily funded players. The authors argue that their mid- and post-training approach—which preserves both plasticity and stability—achieves gains comparable to multiple successive model generations at substantially lower compute and personnel budgets. Thomson performs competitively with recent frontier models on agentic tasks, safety, legal, tax, multilingual, and deep research benchmarks, while largely avoiding the forgetting problems common to narrow domain adaptation. The work directly addresses the concept of SovereignAI, enabling more organizations to independently build, deploy, and govern their own AI systems.
- Enterprise
- AI policy
Research
When Safety Becomes a Competitive Disadvantage: The Structural Dangers of the Frontier AI Race
Dean Gary Egan
Zenodo (CERN European Organization for Nuclear Research) · 2026-08-27
This paper argues that frontier AI development creates a structural race hazard where safety-conscious behavior—such as delaying deployment, conducting deeper evaluations, or accepting oversight—imposes concentrated costs on cautious actors while spreading benefits to competitors and the public. The authors identify seven conditions that intensify this hazard and review evidence from independent assessments and developers' own safety frameworks, finding that leading actors themselves acknowledge competitor-dependent safeguards and potential races to the bottom. The paper proposes a 'Race Hazard Audit' and a governance design standard requiring that restraint be reciprocal, verifiable, competitively survivable, and enforceable. The core concern is that competitive dynamics may systematically select against the very precautions all participants depend on.
- AI policy
Research
When Safety Becomes a Competitive Disadvantage: The Structural Dangers of the Frontier AI Race
Dean Gary Egan
Zenodo (CERN European Organization for Nuclear Research) · 2026-08-27
This paper analyzes why AI safety measures can become a competitive disadvantage in the frontier AI race, identifying seven structural conditions—such as speed premiums, externalised harm, and weak reciprocity—that create incentives for developers to underinvest in safety precautions. Drawing on independent assessments and developers' own safety frameworks, the authors find that leading actors acknowledge competitor-dependent safeguards and potential races to the bottom, even if internal decision-making remains largely unobservable. The paper proposes a 'Race Hazard Audit' and a governance design standard requiring that safety restraint be made reciprocal, verifiable, competitively survivable, and enforceable. The work matters because it provides a mechanism-based framework for policymakers to prevent competitive dynamics from systematically selecting against the very precautions that protect all participants and the public.
- AI policy
Research
Precision psychiatry in clinical practice: What is clinically actionable, what is promising, and what remains experimental?
Júlio Torales, Marcelo O’Higgins, Iván Barrios et al.
International Review of Psychiatry · 2026-08-27
This structured narrative review evaluates the clinical readiness of precision psychiatry approaches—ranging from pharmacogenetics and digital phenotyping to AI-assisted decision support and multimodal prediction models—using a framework covering validity, replication, actionability, and equity. The review finds that only a few approaches (e.g., measurement-based care, therapeutic drug monitoring, selected pharmacogenetic interactions) are ready for clinical use, while AI-assisted decision support and neuroimaging markers are promising but not yet routine, and automated suicide prediction or fully automated treatment selection remain experimental. Major barriers across all domains include poor external validation, limited evidence of patient benefit, underrepresentation of diverse populations, and unresolved accountability concerns. The findings matter for quality assurance and policy by setting clear benchmarks for when and how AI and precision tools should be adopted in psychiatric care.
- Quality assurance
- AI policy
Research
Developing a Neuroimaging Training Framework and Certificate Program
Aneta Kielar, Dianne Patterson, Thomas Hicks
PsyArXiv (OSF Preprints) · 2026-08-27
This paper describes the design and evolution of a fully asynchronous neuroimaging education framework at the University of Arizona that trains graduate and undergraduate students, research staff, and faculty in computational skills such as BIDS, Unix, HPC, JupyterLab, and containerization. The program offers both à la carte lessons and a complete neuroimaging certificate path to accommodate learners who prioritize flexibility over formal credentialing. A notable contribution is the framework's approach to AI-resilient assessment, replacing multiple-choice questions with interactive matching questions and structured generative AI workflows while retaining hands-on practicals. The paper positions this case study as a template for institutions seeking to deliver targeted, on-demand computational neuroimaging training to broad and diverse audiences.
- Certifications
- Workforce
Research
Gender, Power, and Law in European Digital Migration Systems
Tatiana Maria Cernicova-Dragomir
Ars æqui · 2026-08-27
This legal analysis examines whether the EU's current regulatory framework—including the Charter of Fundamental Rights, the GDPR, and the AI Act—adequately protects migrant women and gender-diverse persons from structural inequalities embedded in digital migration systems such as biometric identification and algorithmic risk assessment. Using a doctrinal methodology informed by feminist legal theory, the paper argues that existing safeguards on data protection, non-discrimination, and administrative justice are insufficient because bias arises not from explicit discriminatory rules but from ostensibly neutral digital infrastructures. The article calls for a more substantive interpretation of equality and stronger procedural accountability in regulating automated border governance tools. Its findings are relevant to ongoing EU policy debates about governing AI-driven migration systems fairly.
- AI policy
Research
Containing AI: from responsible to critical AI governance
Paul Schütze, Benedetta Brevini
AI & Society · 2026-08-27
This paper critiques mainstream responsible AI (RAI) governance frameworks, arguing they fail to genuinely address AI's environmental and social costs and instead create an illusion of accountability—sometimes producing a regulatory vacuum rather than meaningful oversight. Using 'sustainable AI' as a case study, the authors propose 'critical AI governance,' which reframes the core question from how to make AI responsible to whether AI is needed at all. The framework rests on three principles: questioning dominant ideologies and economic imperatives, prioritizing social and political solutions over technological fixes, and investing in public digital infrastructure and independent research. The paper has direct implications for how governments and institutions design and enforce AI policy.
- AI policy
Research
Artificial Intelligence and Its Influences on Enterprise Employees
Zuhao Yu
Empirical Studies in Econimics and Finance · 2026-08-27
This systematic review synthesizes 186 peer-reviewed studies (January 2024–May 2026) to map how AI and generative AI affect enterprise employees across productivity and psychological dimensions. The findings show AI both boosts operational efficiency by automating routine tasks and heightens job insecurity, reducing job satisfaction and well-being—with frontline workers bearing disproportionately greater risks than managers. Organizational AI training and transparent algorithmic governance are identified as effective mitigants. The paper develops a theoretical framework and offers practical guidance for human resource management during intelligent transformation.
- Workforce
- Enterprise
Research
Governing at a distance: The EU AI act and GDPR as twin pillars of digital law and corporate governance
Maria de L. Haynes, Akram Al Matarneh
Corporate Law & Governance Review · 2026-08-27
This article examines how the EU AI Act and GDPR function together as complementary regulatory frameworks that reshape corporate governance well beyond EU borders. Using doctrinal and comparative legal methodology, the study finds that both instruments embed compliance obligations—including risk management systems, internal controls, board oversight, and accountability processes—directly into corporate practice, a principle the authors call 'governance-by-design.' The paper concludes that together the two frameworks enable the EU to govern corporate conduct at a distance globally, offering an analytical framework for both academics and practitioners navigating AI and data regulation.
- AI policy
- Enterprise
Research
Multisite Implementation of a Digital Wound Model of Care: A Post‐Implementation Multimethods Evaluation of Patient and Clinician Perspectives and Lessons Learned
Michelle Barakat‐Johnson, Clementine Mak, Michelle Lai et al.
International Wound Journal · 2026-08-27
This Australian multisite study evaluated a district-wide digital wound care model combining an AI-enabled application with a virtual command centre across nine facilities. High patient satisfaction was reported—91% rated care as excellent or good—and clinicians valued improved documentation consistency, though barriers like poor connectivity, time pressures, and training burden were identified. The findings demonstrate strong acceptability of AI-assisted wound care delivery while flagging patient involvement in decision-making and clinician workflow integration as areas needing further development.
- Quality assurance
- Workforce
Research
Not Afraid of AI yet: Indonesian TV Journalists on Negotiating Roles in AI-Assisted Newsrooms
Yearry Panji Setianto, Rossalyn Asmarantika, Veronika et al.
Media and Communication · 2026-08-27
Drawing on interviews with 20 Indonesian television journalists across 12 national newsrooms, this study finds that journalists largely view AI as a supplementary tool for routine production tasks rather than a replacement for core journalistic functions such as editorial judgment, verification, and contextual interpretation. Journalists see video capture and accountability reporting as irreplaceably human, even as they navigate economic pressures and newsroom restructuring. The findings illuminate how journalists in a Global South media context negotiate professional identity and role perceptions amid AI adoption, balancing established journalistic values against changing industry realities.
- Workforce
Research
Readability of U.S. food handler training materials: a natural language processing analysis of worker study guides and the federal Food Code
Morris Brako, Anirudh R. Naig
Frontiers in Public Health · 2026-08-27
This study used a natural language processing pipeline to assess the reading difficulty of 14 food handler training study guides across 11 U.S. jurisdictions, comparing them to the 2022 FDA Food Code. Results showed that 13 of 14 guides exceeded the recommended sixth-grade Flesch–Kincaid readability target, and the FDA Food Code was more demanding than every guide. Washington's Food Worker Manual was the sole exception, demonstrating that accessible materials are achievable in this domain. The findings raise concerns about whether certification training materials are actually usable by the workers required to study them, and highlight audit gaps where content is delegated to private vendors.
- Certifications
- Workforce
Research
Trusting the AI, Crafting the Show: Algorithmic Dependence and Human–AI Collaboration in Variety-Show Post-Production Across Chinese OTT Platforms
Fang Wang, Yazhou Huang, Qianxun Jiang et al.
International Journal of Human-Computer Interaction · 2026-08-27
This study examines how post-production workers on Chinese OTT variety shows adopt and come to depend on AI tools, using a mixed-methods design combining survey data from 443 respondents (analyzed via PLS-SEM) with 20 qualitative interviews. Findings show that trust in AI—shaped by performance expectancy, effort expectancy, interpretability, controllability, and verifiability—drives behavioral intention and use, which in turn leads to algorithmic dependence as a post-adoption outcome. AI anxiety moderates how workers evaluate AI systems, and the study finds that ease of use can paradoxically trigger resistance when workers feel it threatens their professional value. The research informs how AI tools can be deployed in creative media production in ways that remain compatible with human judgment, occupational sustainability, and professional responsibility.
- Workforce
- Enterprise
Research
Expert-Guided Visual Correction for Characterizing Diagnostic Performance and Error Patterns of Multimodal Large Language Models Using Periodontal In-Service Examination Images
Prita Abhay Dhaimade, Robin Henderson
medRxiv · 2026-08-27
This study benchmarked six multimodal large language models (MLLMs) on 50 image-based multiple-choice questions from the American Academy of Periodontology In-Service Examination, using a two-phase design where models first answered independently and then re-answered incorrect questions after receiving expert-validated image descriptions. By distinguishing perceptual errors (faulty visual feature extraction) from cognitive errors (flawed reasoning despite accurate perception), the authors found that aggregate accuracy scores mask mechanistically distinct failure modes—some errors resolved with corrected visual input while others persisted regardless. The framework demonstrates that safe clinical deployment and model improvement require separately characterizing perceptual versus reasoning failures, with direct implications for AI-augmented dental education and competency assessment.
- Quality assurance
- Certifications
Research
Strategic competitiveness between the EU and the United States in artificial intelligence development – human capital, legal frameworks and data
Robert Lizak
Kortowski Przegląd Prawniczy · 2026-08-27
This article compares the strategic competitiveness of the EU and the United States in AI development across three dimensions: human capital availability, regulatory environment, and data access. Grounded in Draghi's Report on European economic competitiveness and key legal instruments including the EU AI Act, GDPR, and US federal laws, the analysis finds that the EU model prioritizes fundamental rights protection and risk management, while the US approach emphasizes innovation, public-private collaboration, and broad data access. The authors conclude that the US's more flexible legal environment accelerates AI commercialization and competitive advantage, and they offer recommendations for the EU to strengthen human capital, simplify regulation, and improve data availability.
- AI policy
Research
Artificial intelligence in social work practice for children and youth: A scoping review
Zhao Lifen, Ying Feng, Yong‐Xin Ruan et al.
International Journal of Social Welfare · 2026-08-27
This scoping review synthesizes evidence on AI applications in social work for children and youth, drawing on 17 studies across eight databases. Only five studies provided empirical evaluations—focused mainly on identifying service users—and their results were mixed, while the remaining studies were theoretical. Across all evidence tiers, AI served a supporting role with social workers retaining final decision-making authority, and key challenges included algorithmic bias, privacy risks, contextual complexity, and unclear accountability. The review highlights the limited and uneven state of evidence, urging responsible AI integration and a clear future research agenda in this domain.
- Workforce
- AI policy
Research
Non‐inferiority analysis of a fetal heart rate artificial intelligence algorithm to registered nurse assessment
Rohit Pardasani, Renee Vitullo, Sara Harris et al.
Pregnancy · 2026-08-27
This retrospective study evaluated whether an AI algorithm using deep learning and rule-based techniques could assess fetal heart rate and uterine activity from cardiotocography (CTG) tracings at a level comparable to registered nurses (RNs). Across data from five U.S. hospitals (2013–2024), the algorithm met a pre-specified 15% non-inferiority threshold versus RN readers on most primary endpoints—including baseline fetal heart rate, accelerations, decelerations, and contraction frequency—though it fell short for contraction duration under the strict criterion. The findings suggest AI could serve as a consistent, objective decision-support tool in obstetric care, potentially reducing the inter- and intra-observer variability that currently affects clinician interpretation of CTG tracings.
- Quality assurance
- Workforce
Research
Developing a Neuroimaging Training Framework and Certificate Program
Dianne Patterson, Thomas Hicks, Aneta Kielar
arXiv · 2026-08-27
This paper describes the design and evolution of an asynchronous neuroimaging training program at the University of Arizona that teaches computational skills—including BIDS, Unix, HPC, JupyterLab, MATLAB, Bash, and containerization—to a diverse cohort of students, staff, and faculty. A key feature is its flexible, à la carte structure that allows learners to pursue individual lessons or a full certificate path. The program also addresses AI's disruption to traditional assessment by replacing multiple-choice questions with interactive matching and structured generative AI workflows while retaining hands-on practicals. The case study offers a replicable template for institutions seeking to deliver targeted, on-demand computational training.
- Certifications
- Workforce
Research
Social support and technological displacement anxiety among ride-hailing drivers: The mediating roles of self-efficacy and qualitative job insecurity
WU Xin-hui, Yan Dai
Acta Psychologica · 2026-08-27
This study of 447 ride-hailing drivers finds that higher social support is significantly associated with lower anxiety about being displaced by autonomous driving technology. Using bootstrap mediation analysis, the researchers identify three psychological pathways through which social support reduces displacement anxiety: via increased self-efficacy, via reduced qualitative job insecurity, and via a sequential chain where social support boosts self-efficacy, which in turn lowers job insecurity, which then reduces anxiety. The authors suggest these findings could inform mental-health and occupational-support interventions for gig workers facing AI-driven disruption.
- Workforce
Research
Users’ perceptions of generative artificial intelligence and copyright infringement: an exploratory analysis
Lateef Ayinde, Krishnaveni Katta, John Oluwaseye Adebayo
Journal of Science and Technology Policy Management · 2026-08-27
This study analyzes 1,435 Reddit comments to understand how everyday users perceive generative AI and copyright infringement. Using topic modeling, sentiment analysis, and statistical methods, the researchers find that most users believe GenAI infringes on copyrighted works, that AI models are trained on such works, and that fair use protections are insufficient. Users generally favor regulation and compensation for original rights-holders rather than an outright ban on GenAI, findings the authors argue should inform both government policy and organizational product decisions.
- AI policy
Research
Bridging the Knowledge, Usage, and Regulation Gap for Artificial Intelligence in Medicine: A Cross-Sectional Survey of Spanish Clinicians and Trainees
Jorge García Condado, Enric Cristòbal-Cóppulo, Mireia Gamundi et al.
Journal of Scientific Innovation in Medicine · 2026-08-27
A cross-sectional survey of 167 Spanish clinicians and medical trainees found that familiarity with AI in medicine averaged only 4.2 out of 10, and awareness of AI regulation was even lower at 2.2 out of 10, despite near-universal recognition of ChatGPT. The study identifies significant educational, generational, and gender gaps—with younger respondents showing higher AI literacy and males reporting greater engagement—that could hinder responsible AI adoption in clinical practice. The authors conclude that interdisciplinary education, inclusive training initiatives, and greater clinician involvement in regulatory processes are needed to close these gaps.
- Workforce
- AI policy
Research
Bridging semantics and clinical fidelity: a section-based assessment of a vision–language model (RadVLM) for chest x-ray report generation
Saleh Alzughaibi
Frontiers in Digital Health · 2026-08-27
This study evaluated RadVLM, a vision-language AI model, generating radiology reports for 3,000 chest X-ray studies, finding that surface-level fluency and semantic similarity in AI-generated Impression sections do not reliably indicate factual correctness in the underlying Findings. The mean RadGraph F1 score of 0.251 revealed substantially imperfect structural accuracy, and the association between semantic similarity metrics and entity-relation fidelity was too weak to be clinically useful. The authors conclude that deploying such AI systems in clinical settings requires explicit entity-relation validation, structured accuracy assessment, and radiologist oversight to protect patient safety.
- Quality assurance
- Certifications
Research
AI Publication Footprint and National AI Readiness: Global Geographic and Income-Based Disparities
Анна Воронцова, Аrtem Аrtyukhov, Nadiia Аrtyukhova et al.
Sustainability · 2026-08-27
This study examines how closely a country's AI research publication output (based on Scopus data) aligns with its overall AI readiness (measured by the IMF AI Preparedness Index) across 173 countries, grouped by region and income level. Using statistical methods including ANOVA, correlation, regression, and cluster analysis, the authors find a moderately strong global association (r = 0.68) between AI publication footprint and national AI readiness, with the strongest alignment in high-income countries and regions like the Americas and Europe. Lower-income countries show weak or statistically insignificant relationships, pointing to persistent structural barriers in digital infrastructure, human capital, and innovation capacity. The findings underscore the need for differentiated, adaptive policy frameworks that account for regional and economic disparities in AI development trajectories.
- AI policy
Research
‘It's here and we can't fight it’: engineering faculty perceptions of generative AI, student use, and the future of engineering education
Pratima Saravanan, Connor Meissner, Jennifer Glenn
Frontiers in Education · 2026-08-27
This qualitative study examined how 16 engineering faculty at a U.S. research-intensive university perceive and respond to generative AI tools like ChatGPT through semi-structured focus groups and thematic analysis. Findings reveal that faculty adopt a posture of 'reluctant engagement,' accepting GenAI as inevitable rather than desirable, while expressing concern about cognitive offloading, erosion of productive struggle, and disruption of assessment systems. Faculty also reported bearing the costs of institutional policy gaps without coordinated support and anticipated significant curricular redesign ahead. The study underscores the need for institutional governance, professional development, and equity-conscious assessment reform in engineering education.
- Workforce
- AI policy
Research
Beyond Benchmark Accuracy: An Enterprise Deployment and Readiness Framework for Large Language Model-Based Text-to-SQL Systems
Dadavali S P, Dr. K. Siddaraju, Dr. Chandrashekar P
IJARCCE · 2026-08-27
This paper introduces ERF-SQL, an architectural framework designed to make LLM-based text-to-SQL systems viable in enterprise settings beyond raw benchmark accuracy. The authors show that even models achieving 85% execution accuracy on standard benchmarks fail critical enterprise requirements—exposing masked columns, ignoring row-level access controls, and leaving no audit trail—and that these are architectural problems, not model quality problems. ERF-SQL addresses this through a versioned semantic contract, a Policy-Aware Query Guard that rewrites SQL syntax trees before execution, and an Enterprise Readiness Index across six dimensions; on their custom evaluation suite (EntSQL-450), the pipeline blocked 97% of unsafe queries, recovered 13–16 accuracy points lost under enterprise constraints, and improved drift resilience from 0.55 to 0.86 at about 0.9 seconds of added latency. The work demonstrates that enterprise deployment readiness for AI systems can be systematically engineered and measured rather than assumed from benchmark performance.
- Enterprise
- Quality assurance