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.
5526 items
Research
Who Gets Heeded? An Obligation-Level Audit of Responsiveness in EPA Rulemaking
Jianing Fan, Yue Yao
arXiv · 2026-08-11
This paper introduces an AI-assisted framework for auditing whether public comments on proposed federal regulations actually influence specific regulatory obligations in final rules, applied to over 70,000 comments across 36 EPA rulemakings. The framework extracts and matches obligations between proposed and final rules, classifies outcomes, and validates each step against human judgment. Key findings show that comment engagement is associated with only modest revision at the obligation level, that supporting versus opposing a rule does not clearly predict outcomes, and that organizational commenters' engagement concentrates in editorial rather than substantive changes. The authors argue the real equity gap lies upstream — in unequal capacity among commenter populations to identify and contest specific legal obligations — rather than in differential agency responsiveness itself.
- AI policy
Research
Lost in the Loop: Who Is the ‘Human’ of the Human in the Loop?
Jake Goldenfein
Cambridge University Press eBooks · 2026-08-11
This chapter critically examines the 'human in the loop' doctrine in automated decision-making, arguing that requirements for human oversight serve primarily political rather than empirical purposes. The author finds little evidence that human oversight actually improves decision outcomes, and through analysis of administrative law cases, shows that invoking 'human in the loop' functions as a legal technology that obscures accountability and limits richer legal understanding of automated systems. The work challenges regulatory orthodoxy by revealing how human oversight requirements can distribute accountability in troubling ways rather than genuinely safeguarding against automation's harms.
- AI policy
Research
Artificial Intelligence in Psychiatry: Five Decades of Progress and Persistent Translational Challenges
Esteban Zavaleta‐Monestel, Luis Guillermo Herrera-Jiménez, Sofía Suárez-Sánchez et al.
Psychiatric Research and Clinical Practice · 2026-08-11
This structured historical review traces AI development in psychiatry from 1972 to 2025, synthesizing evidence across paradigms ranging from rule-based expert systems to large language models. Despite five decades of technical advances, the authors find that recurring barriers—including diagnostic heterogeneity, limited external validation, poor transportability, interpretability gaps, and equity concerns—have prevented widespread clinical adoption. Progress is characterized as cyclical rather than linear, with successive technological waves reproducing the same unresolved challenges. The review concludes that future clinical impact will depend on clearer target validity, prospective implementation trials, patient-centered evaluation, and mental health-specific governance rather than algorithmic sophistication alone.
- AI policy
- Quality assurance
Research
SafeCA: Safe Cross-Attention Localization and Regulation for Text-to-Video Jailbreak Defense
Siyuan Liang, Yupeng Qiu, Junfeng Fang et al.
arXiv (Cornell University) · 2026-08-11
SafeCA is a defense mechanism for text-to-video generative models that targets jailbreak attacks—attempts to make these models produce harmful or inappropriate content. By analyzing cross-attention feature spaces, the authors identify a 'cumulative separation effect' between clean and jailbreak samples during the diffusion process, enabling feature-level intervention via attention masking, energy normalization, and a lightweight semantic adapter. Experimental results show SafeCA reduces jailbreak success rates by approximately 20% on mainstream text-to-video models while adding only 0.1 seconds of inference overhead and preserving text-video semantic consistency. This work advances quality-assurance and policy-relevant safeguards for deployed generative AI systems.
- Quality assurance
- AI policy
Research
Agentic Configuration Management (ACM): A Reference Configuration Model for Governed Agentic Systems
Audrey Quessada-Vial
arXiv (Cornell University) · 2026-08-11
This paper introduces Agentic Configuration Management (ACM), a framework-independent governance and configuration model for AI agentic systems composed of heterogeneous agents, tools, models, and workflows. ACM provides typed versioned configuration items, immutable revisions, dependency-aware impact propagation, and runtime provenance, normalized into a canonical Configuration Graph. A Python reference implementation with adapters for LangGraph, CrewAI, and the OpenAI Agents SDK was evaluated across 27 governance scenarios and nine quantitative impact-propagation cases, demonstrating reproducible and auditable governance outcomes across frameworks. The work provides evidence that common governance semantics can support reproducibility, auditability, and interoperability across heterogeneous agentic systems.
- Quality assurance
- AI policy
Research
On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models
Md Jafrin Hossain, Mohammad Arif Hossain, Nirwan Ansari
arXiv (Cornell University) · 2026-08-11
This paper presents a systematic literature review (PRISMA 2020) of 85 papers on agentic large language model security, finding that attack research outpaces defense work by 3.9:1 and that perception-layer vulnerabilities dominate the literature while action-layer risks like tool misuse and code injection are severely understudied relative to real-world threat. The authors introduce a four-layer taxonomy covering 13 vulnerability types and identify architectural coupling—weak isolation allowing vulnerabilities to propagate across layers—as the root cause of many agentic LLM security failures. The findings matter because autonomous LLM agents now operate with real-world privileges (API calls, file modification, database queries), making unmitigated security gaps a direct enterprise and policy risk.
- Enterprise
- AI policy
Research
Challenges and opportunities for advanced manufacturing to deliver war-winning technology to the warfighter
Clara Mock, Jian Yu, Eric Wetzel et al.
arXiv · 2026-08-11
This report examines how Advanced Manufacturing (AdvM) can address global supply chain vulnerabilities and contested logistics environments by enabling both domestic Defense Industrial Base production and distributed, Point-of-Need manufacturing on the battlefield. The authors identify persistent barriers to AdvM adoption—including material limitations, process transferability, and certification bottlenecks—and propose solutions across four domains: novel feedstock development, AI-driven digital quality assurance (predictive modeling, in-situ monitoring, digital twins), workforce development through academia-industry-military collaboration, and performance-based qualification standards. The paper argues that integrating these elements can enable highly autonomous manufacturing to support continuous military mission readiness. The findings are directly relevant to workforce training, enterprise-level defense industrial capacity, quality assurance through digital frameworks, and certification pathway reform.
- Workforce
- Enterprise
- Quality assurance
- Certifications
Research
Professional roles, gender, and attitudes toward generative AI: evidence from Japan
Eiji Yamamura, Fumio Ohtake
AI and Ethics · 2026-08-11
Using a large-scale survey of 3,433 respondents in Japan (early 2024), this study finds that attitudes toward generative AI vary substantially by occupation and gender. Managers and ordinary employees tend to hold more favorable views of generative AI, while teachers emphasize the ongoing importance of human judgment and learning. Gender differences are also significant: female managers and employees are less favorable toward generative AI than male peers, while female teachers show the reverse pattern. The findings highlight how occupational identity and gender shape workforce readiness and receptivity to AI adoption.
- Workforce
- AI policy
Research
From Traditional Audits to Digital Audits: A Systematic Review of the Impacts and Driving Factors
Christine Belgina Saurmauli, Krisna Puji Rahmayanti
Journal Of Social Research · 2026-08-11
This systematic review synthesizes empirical evidence from 33 studies (2015–2026) on how digital technologies—including AI, robotic process automation, blockchain, big data, and audit analytics—are transforming audit practice. The findings show these tools generally improve audit effectiveness and efficiency, strengthen internal controls, and reduce errors and financial statement restatements, while repositioning auditors as more strategic, data-driven professionals. Adoption success depends heavily on technological infrastructure, data governance, organizational capabilities, leadership support, and auditor competencies, indicating digitalization is neither neutral nor automatic. The paper has direct implications for audit firms, internal audit units, public sector institutions, and regulators designing digital audit strategies.
- Quality assurance
- Certifications
- AI policy
Research
Balancing AI Data and Consumer Rights: The Colombian Context
J.W. Vásquez, Rene Alvarez‐Orozco
Latin American Policy · 2026-08-11
This paper examines surveillance capitalism in Colombia, where AI-driven data collection by corporations in telecommunications, retail, finance, and digital platforms raises serious privacy and consumer rights concerns. Using computational simulations and case studies, the authors quantify risks from opaque consent mechanisms, algorithmic bias, and data breach vulnerabilities. The study evaluates Colombia's AI governance framework—specifically CONPES 4144 de 2025—finding significant enforcement and implementation shortcomings, and calls for stronger regulatory frameworks to protect consumer rights while supporting innovation.
- AI policy
Research
The AI–Climate Nexus in International Relations: Climate Diplomacy, Energy Transition, and Emerging Technology Governance
Hina Ahmad, Kinza Kamran, Fizzah Muhammad
Journal of Global Social Transformation · 2026-08-11
This study examines how AI is reshaping international climate diplomacy, energy transition, and geopolitical power dynamics through analysis of 127 policy and academic sources and four comparative case studies. It finds that while AI improves emissions verification and renewable grid optimization, it also introduces risks such as large computational carbon footprints, digital infrastructure lock-in, and geopolitical shifts favoring states with computational dominance and control of critical minerals. The research highlights that the Global South faces acute risks of data colonialism and algorithmic marginalization under fragmented current governance regimes. The paper calls for a dedicated UN AI–Climate Governance Interface that embeds common but differentiated responsibilities into global AI policy.
- AI policy
Research
The integration of artificial intelligence in the academic writing of EFL students in higher education: a case study at Fergana State University
Abdulkhay Kosimov Akhadali Ugli, Abdullajon Nematov, Markhabo Shokirova Sharifovna
Cogent Education · 2026-08-11
This mixed-methods case study of 351 EFL students at Fergana State University, Uzbekistan, examined how AI writing tools (ChatGPT, Grammarly, QuillBot) affect academic writing quality and cognitive independence. Quantitative results showed AI assistance improved grammatical accuracy (35% error reduction) and lexical sophistication (53% increase in Academic Word List coverage), but was associated with declines in argumentative claims (24%) and originality. Qualitative findings highlighted tensions between AI as scaffolding versus substitution and a perceived performance–competence gap, suggesting AI may boost surface-level writing while undermining independent academic reasoning.
- Workforce
- Quality assurance
Research
Integrating LLM with consortium blockchain for personalized and verifiable online education in higher education
Fuan Xiao, Jiahui Huang, Jia-Xin Huang et al.
International Journal of Educational Technology in Higher Education · 2026-08-11
This paper proposes a framework that combines large language models (LLMs) with a permissioned consortium blockchain to deliver personalized online education while addressing LLM reliability problems such as hallucinations and output inconsistency. The blockchain serves as a tamper-proof ledger that records learning interactions, AI-generated content, and academic credentials, creating an auditable trail that can assign accountability when AI errors cause poor learning outcomes. The framework aims to make AI-driven education both trustworthy and verifiable, which has direct implications for how institutions certify learning and validate credentials. The work matters because it provides a concrete architectural response to the accountability gap in deploying LLMs at scale in higher education.
- Certifications
- Quality assurance
Research
Uso inadecuado de la inteligencia artificial en el proceso de enseñanza-aprendizaje: Un estudio en instituciones educativas del Ecuador
Angelica Liliana Zhunio Zhunio, María Tráncito Chela Tualombo, María Beatriz Villalobos Veloz et al.
Neosapiencia Revista especializada en Ciencias de la Educación · 2026-08-11
This study from Ecuador surveyed 350 higher-education students to assess the impact of inappropriate generative AI use on learning outcomes. Key findings include that 64.86% of students submitted AI-generated essays without modification, 72.86% preferred receiving final answers rather than understanding procedures, and a Pearson correlation of 0.79 was found between unregulated technological dependence and degradation in critical analysis, writing, and retention. The authors conclude that uncritical AI reliance promotes intellectual minimum effort and erodes professional-quality competencies, urging institutions to implement assessments based on human reasoning and oral defense rather than simple prohibition.
- Workforce
- Quality assurance
Research
Artificial Intelligence Investment and Enterprise Green Innovation Efficiency
Feng Qianhao
Advances in Economics Management and Political Sciences · 2026-08-11
Using a panel of China's A-share listed companies from 2007 to 2023, this study finds that greater AI investment and disclosure significantly improve enterprise green innovation efficiency: each logarithmic unit increase in AI-related word frequency raises green innovation efficiency by roughly 8%, and a one-standard-deviation increase in investment level improves it by about 4%, both significant at the 1% level. The effects are stronger for state-owned, large-scale, and manufacturing enterprises, and are amplified by higher asset-liability ratios, suggesting financing capacity is a key moderator. Robustness checks using high-dimensional fixed effects and a difference-in-differences design around China's National New-Generation AI Innovation Pilot Zones confirm the findings. The paper offers empirical guidance for enterprises optimizing AI investment strategies to support green transformation.
- Enterprise
Research
Clinical predictive artificial intelligence evaluation: A narrative review of trial designs and practical considerations
Maxime Fosset, Joris Pensier, Boris Jung et al.
PLOS Digital Health · 2026-08-11
This narrative review argues that conventional randomized controlled trials are poorly suited for evaluating clinical AI predictive models because of their static design and inability to accommodate evolving algorithms. The authors propose a three-part framework encompassing performance monitoring, clinical impact monitoring, and scientific evidence generation, linked through a governance-driven escalation protocol that specifies when monitoring signals should trigger formal trials. The framework integrates adaptive and pragmatic trial designs with causal inference methods, emphasizing patient-centered outcomes, health equity, and workflow integration. The work provides actionable guidance for clinicians and trialists seeking to rigorously assess AI tools in routine clinical settings.
- Quality assurance
- Certifications
- AI policy
Research
Perceived skill devaluation as an indirect statistical pathway between perceived generative AI impact and algorithmic anxiety among college students: associations with adaptive coping strategies
Dou ChenXu, Zhang Yinuo, Ji Chenao et al.
Frontiers in Education · 2026-08-11
This study surveyed college students to examine how perceiving generative AI as impactful on the job market relates to anxiety and coping. Results from structural equation modeling show that perceived AI impact is positively linked to feeling that one's skills are being devalued, which in turn is associated with higher algorithmic anxiety. Career self-efficacy and growth mindset were not associated with reduced anxiety but were positively linked to adaptive coping strategies, suggesting that psychological resources matter more for coping than for anxiety reduction. The findings highlight workforce-relevant concerns about how students appraise AI-driven threats to their career competencies.
- Workforce
Research
Employer perspectives on AI in SMEs: policy insights for productivity and sustainable careers
Tony Fang, J.A. Harrison
Career Development International · 2026-08-11
This practitioner insights paper surveys 1,700 business owners and senior managers—with a focus on SMEs in Atlantic Canada—to understand how employers view AI adoption. Findings show that while employers associate AI with productivity and efficiency gains, adoption is uneven across firms and regions, and many employers remain uncertain about future skill needs and focus primarily on compliance training. The paper argues that policymakers should strengthen digital capabilities, workforce planning systems, and regional skills ecosystems to ensure AI adoption supports both productivity and sustainable career outcomes.
- Workforce
- AI policy
Research
Risks of artificial intelligence adoption for employment in Russia
Rostislav Kapeliushnikov
Voprosy Ekonomiki · 2026-08-11
This study estimates the risk of AI adoption for employment in Russia using International Labour Organization scoring methods applied to Rosstat Labor Force Survey microdata. The average AI risk index for Russia is 0.3, with roughly one in ten jobs facing high AI risk and one in three facing significant risk — lower than developed countries but higher than developing ones. Less than 1% of jobs face full automation, and the impact is concentrated in a narrow range of occupations, primarily clerical workers. The authors conclude that AI adoption in Russia is more likely to reconfigure tasks within existing occupations than eliminate them wholesale, suggesting fears of mass technological unemployment are overstated.
- Workforce
- AI policy
Research
Artificial Intelligence and Family Law: The Impact of the EU AI Act Regulatory Framework and Principles on the Use of AI in Family Proceedings
Michał A. Piegzik
European Journal of Risk Regulation · 2026-08-11
This article examines how the EU AI Act, adopted in August 2024, applies to the use of AI tools in family court proceedings across EU Member States. Using doctrinal, normative, and socio-legal research methods, the authors critically assess the new regulatory framework's conceptual and practical limitations when applied to family law contexts, where AI has been proposed as a solution to growing caseload pressures and access-to-justice challenges. The paper identifies scholarly gaps and explores implications of the EU AI regulatory model both within and beyond individual Member States.
- AI policy
Research
The rise of AI in weather and climate information and its impact on global inequality
Amirpasha Mozaffari, Amanda Duarte, Lina Teckentrup et al.
npj Climate Action · 2026-08-11
This paper argues that AI-driven weather and climate forecasting systems are being developed almost exclusively in the Global North, creating a risk of entrenching and amplifying existing inequalities in climate information access. The authors identify disparities across the full pipeline—from biased training data to unrepresentative model validation—that disproportionately harm vulnerable regions in the Global South. They propose remedies including a Climate Digital Public Infrastructure, well-being-centered evaluation metrics, and inclusive knowledge co-production to promote resilience rather than inequity. The findings carry direct implications for climate policy and the governance of AI in public-interest domains.
- AI policy
Research
Does it pay off to use GenAI in the Russian labor market?
Ksenia Rozhkova, Sergey Roshchin, Yana Roshchina
Voprosy Ekonomiki · 2026-08-11
Using Russian panel survey data (RLMS-HSE, 2023–2024) and methods including propensity score matching and fixed-effects regression, this paper estimates wage returns to generative AI use in Russia's labor market. On average, workers who use GenAI earn roughly 13.4% more, rising to 21.2% for highly qualified specialists; fixed-effects models find returns only for regular users, ranging from 17.2% (full sample) to 41.8% (highly qualified specialists). The results suggest the wage premium is partly pre-determined by the types of jobs where GenAI is applicable, and that only about 11% of employed Russians had adopted such tools by 2023–2024. The findings matter for understanding how AI adoption translates into measurable labor market inequality and earnings differences across skill levels.
- Workforce
Research
AI skills demand in the Russian labor market
Andrei Ternikov, Vera Maltseva, K. A. Iliashchenko et al.
Voprosy Ekonomiki · 2026-08-11
Analyzing over 700,000 Russian online job postings from 2021 to 2025, this paper tracks the diffusion of AI skills and their wage effects in a major non-Western economy. Although AI skills appear in fewer than 3% of postings, demand is rising sharply in high-tech industries, while the wage premium for AI skills fell from 31.8% in 2022 to 11.8% in 2025, indicating market stabilization. The study finds no evidence of labor substitution; instead, AI skills complement analytical and managerial competencies, especially in non-IT roles. These findings challenge job polarization narratives and support a 'soft complementarity' model of AI and human labor.
- Workforce
Research
Accelerating ambient AI scribe enterprise-scale deployment: Cleveland Clinic’s novel approach to health system-industry partnership
Amy Merlino, Abigail Blue, Eric Boose et al.
npj Health Systems · 2026-08-11
This paper describes Cleveland Clinic's four-pillar partnership framework—covering governance, training and onboarding, support, and real-time learning—used to deploy AI ambient scribes to over 4,000 ambulatory care clinicians in just four months. The authors present this vendor-health system model as a replicable blueprint for other health systems seeking rapid, enterprise-wide AI scribe rollout. The work highlights the organizational and operational strategies that enabled efficient large-scale deployment rather than evaluating clinical outcomes.
- Enterprise
- Workforce
Research
Artificial Intelligence (AI)-driven entrepreneurial strategies and customer-related performance of SMEs in Sanchez Mira, Cagayan, Philippines: an exploratory mixed-methods case study
Michael Sacramed
Future Business Journal · 2026-08-11
This mixed-methods study of 50 SMEs in rural Sanchez Mira, Cagayan, Philippines finds that localized AI adoption—including NLP chatbots and predictive analytics—is positively associated with adaptive entrepreneurial strategies (B=0.61, R²=.37) and shows statistically significant links to improved customer satisfaction, retention, and responsiveness. Qualitative findings reveal a 'rural paradox' where impersonal AI interfaces conflict with community-centric kinship cultures, and low digital literacy forces informal, locally adapted strategies that diverge from Western entrepreneurial frameworks. The study argues these findings offer a transferable 'diagnostic blueprint' for digital transformation in peripheral emerging economies across the Global South, with implications for how enterprise AI adoption should be tailored to rural, resource-constrained contexts.
- Enterprise