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.
5218 items
Research
Closing the Empirical Loop: Autonomous AI Agents Conduct End‐to‐end Research With Human Participants
Gabrielle Wehr, Reuben Rideaux, Amaya Fox et al.
Advanced Science · 2026-09-14
This paper demonstrates that a domain-agnostic agentic AI system can independently conduct end-to-end psychological research — from hypothesis generation and online data collection with 288 human participants, through coding analysis pipelines, to producing completed manuscripts — without requiring substantial human oversight. The system autonomously designed and executed three studies on visual working memory, mental rotation, and imagery vividness, achieving methodological rigor comparable to experienced researchers, though with limitations in conceptual nuance. The findings raise important questions about the future role of human researchers, scientific credit attribution, and governance of autonomous AI in research workflows. This has direct implications for research workforce dynamics and policy around AI oversight in scientific practice.
- Workforce
- AI policy
Research
AI Competencies and Lifelong Learning for Vocational Teachers: Evidence from Thailand
Natchaya Sommartdejsakul, Phongsak Phakamach, Songdet Sonjai et al.
Journal of Education and Learning Reviews · 2026-09-14
This study develops and validates an empirical framework for AI competencies among Thai vocational teachers in the Eastern Economic Corridor (EEC), using exploratory factor analysis with 504 personnel and interviews with 12 administrators. The analysis validated three domains (cognitive, psychomotor, and affective), seven subcomponents, and 174 indicators explaining over 82% of cumulative variance in each domain. The resulting PIERI framework—Planning, Implementation, Evaluation, Reflection, and Improvement—offers a structured pathway for professional development and teacher upskilling. The findings directly support national vocational standards and workforce-readiness priorities in Thailand's EEC.
- Workforce
- Certifications
Research
AI, Democracy and Environmental Justice in Africa
Oluwakorede Ajibona
The Paris Journal on AI & Digital Ethics · 2026-09-14
This paper examines the ecological risks posed by expanding AI data centers across Africa, including land clearing, water consumption, and biodiversity loss. It argues that liberal democratic systems in many African contexts suffer from 'negotiated capture,' where powerful socio-economic interests sideline environmental concerns in infrastructure decisions. As a remedy, the author proposes an African communalism framework—rooted in collective well-being and relational interdependence—as a normative basis for environmental justice in AI governance on the continent.
- AI policy
Research
A Scoping Review of Generative Artificial Intelligence Boundaries in Educational Assessment Systems
ALI MIKAEILI, Reyhaneh Bastani, Soroush Sabbaghan
Canadian Journal of Learning and Technology · 2026-09-14
This scoping review of 43 peer-reviewed studies (drawn from 1,360 records) maps how generative AI is being used in educational assessment—covering scoring, feedback, item design, analytic coding, and integrity monitoring—and identifies the boundaries under which such use is considered appropriate. The review finds that fully autonomous AI decision-making in high-stakes assessments is not supported; instead, hybrid human–AI configurations dominate, with acceptable use framed as conditional on validity, reliability, fairness, interpretability, privacy, and governance. These findings matter because they clarify where institutional and human oversight responsibilities must remain, directly informing how assessment systems should be designed and governed.
- Quality assurance
- AI policy
Research
The Impact of Artificial Intelligence on Firms’ Critical Digital Technology Innovation: A Quasi-Natural Experiment Based on National New-Generation AI Innovation Pilot Zones
Kehao Dong
Journal of innovation and development · 2026-09-14
Using China's National New-Generation AI Innovation Development Zones as a quasi-natural experiment, this study applies a staggered difference-in-differences model to panel data of A-share listed firms to assess how AI pilot policies affect corporate digital technology innovation. Results show that AI pilot zones significantly boost firms' critical digital technology innovation performance, with stronger effects for non-manufacturing firms, non-high-tech enterprises, and firms in highly competitive industries. The findings provide micro-level causal evidence that government-designated AI policy zones can meaningfully drive enterprise-level digital innovation outcomes.
- AI policy
- Enterprise
Research
Co-design of a trustworthy AI-based prognostic tool for predicting patient outcome in acute stroke
Elizabeth Hofvenschioeld, Adam Hilbert, Cathrine K. T. Bui et al.
AI and Ethics · 2026-09-14
This paper presents a case study applying Z-Inspection®, an ethically aligned co-design methodology, to the early design of an AI-based prognostic tool for acute ischaemic stroke within the Horizon Europe VALIDATE project. An interdisciplinary team identified 22 ethical issues, 12 dilemmas, 18 risks, and 48 derived requirements, all mapped to the European Commission's trustworthy AI principles. The study documents how high-level AI ethics principles were translated into context-specific technical, clinical, governance, and organisational requirements for a clinical decision support system. The published requirements are intended to support critical scrutiny and potential adoption in other healthcare AI projects.
- AI policy
- Quality assurance
Research
From Fear to Fluency: Cultivating AI Confidence and Socio-Economic Wellbeing in the Workforce
Narmadha Kamalakannan
The Paris Journal on AI & Digital Ethics · 2026-09-14
This mixed-methods study examines how organizations can move employees from fear to confident engagement with generative AI, drawing on surveys of 217 professionals and interviews with 15 mid-level managers across French, Indian, and American contexts. Key findings highlight that leadership communication and tailored training—rather than generic programs—are central to building psychological safety and skill confidence during AI adoption. The paper proposes the AI Readiness Scan, a six-dimension diagnostic tool covering psychological safety, intercultural contextualisation, regulatory compliance, skill confidence, change resilience, and leadership AI readiness, to help organizations assess and act on evidence before and during AI transformation. The framework integrates psychological safety theory, responsible AI governance, and international labour considerations into actionable guidance for managers and policymakers.
- Workforce
- AI policy
Research
To What End? "Neurodata" Boundaries and Their Implications for Policy and Regulation
Sara Berger, Iris McCall, Alexis Baria
The Paris Journal on AI & Digital Ethics · 2026-09-14
This paper challenges the assumption that 'neurodata'—data about the structure or function of the nervous system—constitutes a stable, well-defined regulatory category. The authors introduce the metaphor of 'neural gravity' to describe how nearly any data type (electrical activity, blood flow, voice, behavior) can be pulled into the neurodata category, making existing hard and soft laws both over- and under-inclusive. Drawing on social anthropology's concepts of boundary objects and boundary work, they argue that current regulatory boundaries reflect existing power structures rather than intrinsic properties of the data. They propose reframing regulation around protecting personhood rather than data type or use, which they argue better addresses collective concerns and respects individuals as ends in themselves.
- AI policy
Research
Regulating the Unseen: AI Accountability in UK Healthcare Sector
Mehmet Unver, Iheanyichukwu Ogu
The Paris Journal on AI & Digital Ethics · 2026-09-14
This paper analyzes accountability gaps in the UK's AI healthcare regulatory landscape, applying a four-layer framework (compliance, report, oversight, and enforcement) to examine how medical device regulation, digital safety standards, transparency mechanisms, and post-market monitoring currently fall short. The analysis finds that fragmentation across these governance tools produces deficiencies in evidence generation, transparency, responsibility attribution, and institutional learning. The authors propose a multi-layered governance model with continuous feedback loops to enable a lifecycle-oriented approach to responsible AI deployment in UK healthcare.
- AI policy
- Certifications
Research
Governing Human-Centric AI in Healthcare
Ida Skubis
arXiv · 2026-09-14
This book investigates what human-centric AI governance looks like in healthcare practice, moving beyond regulatory principles to organizational realities. Drawing on empirical research with 527 respondents, it examines perceptions of humanoid robots in healthcare and finds that acceptance depends on trust, ethics, patient safety, and preserving human interaction—not just expected benefits. The authors introduce two original frameworks: the Human-Centric Robot Acceptance Index (HCRAI) and the Risk Gradient Model of Humanoid Robot Task Acceptance, which together help organizations assess readiness and task suitability for robot integration. The work bridges the EU AI Act's human-centric principles with the practical challenges healthcare managers and policymakers face in deploying AI responsibly.
- AI policy
- Enterprise
Research
Impact of an AI workshop on knowledge and attitudes toward AI in scientific publishing among surgeons at an international abdominal wall surgery congress
Mireia Verdaguer-Tremolosa, Georgia Kotoreni, Simon Hoggart et al.
Journal of Abdominal Wall Surgery · 2026-09-14
This pre-post survey study evaluated a focused educational workshop on AI in scientific publishing conducted at the JAWS Workshop 2026 for abdominal wall surgeons. Before the workshop, nearly 90% of participants already used AI tools for tasks like language editing, manuscript structuring, and literature summarization, yet most reported only basic or no knowledge of responsible use. After two educational presentations, self-reported knowledge scores improved in 52.9% of matched participants (p=0.0005), and post-workshop over 92% reported better understanding of AI limitations, 97.6% committed to verifying AI-generated references, and 85-88% supported disclosure requirements and society guidelines. The findings suggest that brief, targeted educational interventions can meaningfully shift knowledge and attitudes toward responsible AI use in scientific publishing, and highlight a potential role for professional societies and journals in formalizing such training.
- AI policy
- Workforce
Research
A Climate Fix? Promotional Discourse on Artificial Intelligence and the Environment Before and After ChatGPT
Théophile Lenoir, Andreï Mogoutov
The Paris Journal on AI & Digital Ethics · 2026-09-14
Analyzing 1,561 press releases from 2019–2025, this study finds that organizational promotional discourse around AI and the environment shifted markedly after ChatGPT's emergence in late 2022. Before that inflection point, AI was framed as a technical fix for climate change and industrial emissions through specific techniques like detection, forecasting, and optimization; afterward, AI's own energy consumption and environmental footprint became the dominant concern. The ICT sector surpassed all traditionally AI-adjacent sectors—transport, agriculture, industry—as the largest producer of environmental AI press releases, and proposed solutions broadened from software capabilities toward hardware improvements, reporting, policy, and investment. The findings reveal a notable vagueness creep in AI discourse and a reorientation of promotional narratives away from environmental benefit toward environmental self-justification.
- AI policy
- Enterprise
Research
Automated prompt enhancement for AI-assisted research in higher education: A controlled pilot study in healthcare education (Preprint)
Nicole Pollock
arXiv · 2026-09-14
This controlled pilot study compared researcher-authored prompts with automatically enhanced versions across ten authentic healthcare education research tasks, finding that mean critic scores rose from 12.7/24 to 19.8/24 after enhancement, and failure-handling improved from 0/3 to 3/3 in every case. The authors caution that automated prompt enhancement does not directly prove better research outputs, and note one enhanced prompt introduced a privacy risk around unanonymised transcript data. The study proposes a provisional Higher Education Research Prompt Quality Assurance Framework and frames automated enhancement as both an AI-literacy scaffold and an upstream governance control. Findings are relevant to how universities manage AI use in research workflows and set quality and policy standards for GenAI-assisted academic work.
- Quality assurance
- AI policy
Research
Secure Autonomous Agents in SAP Systems: A Control-Property Review Evaluated on a Reconstructed SAP FI Authorization Model
Bindiya Priyadarshini, Martin Pankraz
Zenodo (CERN European Organization for Nuclear Research) · 2026-09-14
This study examines whether the common enterprise assumption — that an AI agent running under a named user's existing authorizations inherits adequate controls — actually holds in SAP Financial Accounting (FI) systems. Using a diagnostic instrument called the Control-Property Review (CPR), the authors ran pre-registered experiments against a reconstructed SAP FI authorization model and found systematic gaps: tolerance controls cannot distinguish individually ordinary documents that collectively breach materiality thresholds, transport governance steps can achieve perfect detection but zero prevention, and audit logs record the human identity rather than the deciding AI process, making attribution impossible regardless of log retention. The paper concludes that autonomous agents do not create these control gaps but move fast enough to make gaps — long present in 'local compliance' assumptions — suddenly visible and exploitable in enterprise ERP environments.
- Enterprise
- Quality assurance
Research
Stakeholder Perspectives on Health System Readiness for Integrating Artificial Intelligence into Public Health in Saudi Arabia
Sultan Alsahli
Healthcare · 2026-09-14
This qualitative study interviewed 32 healthcare professionals, health informatics experts, and policymakers in Saudi Arabia to assess health system readiness for integrating AI into public health. Four themes emerged: infrastructure readiness (with interoperability gaps), limited workforce AI literacy, data governance and ethics challenges (including privacy and algorithmic bias), and strategic opportunities such as disease surveillance and decision support. The findings suggest that responsible AI integration requires coordinated investment in interoperable infrastructure, role-specific training, and clear regulatory frameworks. The authors argue these insights can inform workforce planning and policy development both in Saudi Arabia and in other rapidly digitizing health systems.
- Workforce
- AI policy
Research
Secure Autonomous Agents in SAP Systems: A Control-Property Review Evaluated on a Reconstructed SAP FI Authorization Model
Bindiya Priyadarshini, Martin Pankraz
Zenodo (CERN European Organization for Nuclear Research) · 2026-09-14
This paper examines whether the common industry assumption — that AI agents operating under a named user's existing ERP authorizations inherit adequate controls — holds up under systematic testing. Using a diagnostic instrument called the Control-Property Review (CPR) and experiments on a reconstructed SAP Financial Accounting (FI) authorization model, the authors find that control guarantees must be evaluated at the level of specific control-property pairs, not controls in isolation. Key findings include that tolerance-group controls cannot distinguish a materiality breach assembled from individually ordinary documents, that transport-governance steps can achieve perfect detection of harmful releases while preventing none, and that audit logs record the human identity rather than the AI agent's decisions, making attribution structurally impossible regardless of log retention. The study matters for enterprise AI deployment because it shows that autonomous agents do not create new security gaps in ERP systems — they move fast enough to make pre-existing gaps between local compliance and global assurance suddenly visible and exploitable.
- Enterprise
- Quality assurance
Research
Human-AI Hybrid Workplace Optimization and Productivity in Labor-Intensive Agricultural Operations in Centre Region of Cameroon: An Empirical Study
Eyong Ako
Journal of Economic Development and Village Building · 2026-09-14
This empirical study surveyed 138 agricultural operators across 15 enterprises in Cameroon's Centre Region to examine how human-AI hybrid collaboration affects productivity and workforce outcomes in labor-intensive farming. Findings show that AI-human task sharing had the strongest correlation with operational productivity (r = 0.485, p < 0.001), and hybrid workplace optimization explained 37.9% of variance in workforce outcomes, with training and skill development as the leading predictor. The research extends Socio-Technical Systems Theory to a Central African agricultural context where a 15% workforce decline and only 1.5% annual productivity growth have been recorded. Results offer practical guidance for agri-businesses and cooperatives on investing in training, organizational support, and role definition to realize productivity gains from human-AI collaboration.
- Workforce
- Enterprise
Research
Human-in-the-Loop Control Planes for Cortex Agents: Policy-Driven Escalation, Approval, and Evidence Capture
Sashank Siwakoti, Bhaskar Chaganti
International Journal of Advanced Artificial Intelligence Research · 2026-09-14
This paper introduces PAVE-CP, a vendor-neutral control plane designed to govern when AI agents must escalate decisions to humans, what evidence must accompany those requests, and how approvals are enforced and recorded. Using a design-science approach, the system combines policy decision points, graded evidence models, approval brokering, and tamper-evident audit logs into a single verifiable control object. In synthetic evaluation over one million proposed actions, PAVE-CP produced 0.36 high-impact unsafe commits per 10,000 proposals compared to 129.79 under role-based autonomy, while routing only 16.54% of proposals to human review. The work addresses a gap in enterprise AI governance by providing an externally enforceable authorization contract rather than relying on prompt guardrails or post-hoc observability.
- Enterprise
- AI policy
Research
Reported and anticipated workforce reconfiguration during artificial intelligence adoption: Firm-level evidence from Slovakia
Peter Štetka, Zuzana Hajduová, Nora Grisáková
Problems and Perspectives in Management · 2026-09-14
Using a cross-sectional survey of 693 AI-engaged Slovak firms, this study finds that AI adoption is more often associated with job creation than elimination (18.0% vs. 9.2%), but the two outcomes frequently co-occur within the same firm at a rate far above chance (odds ratio 7.23), meaning aggregate net employment figures mask simultaneous hiring and cutting. More advanced AI adoption stages are linked to higher odds of job creation, while employee job-threat concern is associated with higher odds of layoffs. The findings highlight that workforce impacts of AI are heterogeneous and firm-specific, and the authors contribute a four-pattern typology of incidence outcomes to help characterize these configurations.
- Workforce
Research
Operationalising Responsible AI Governance in Financial Crime Compliance: An Evidence-Based Control Framework for Financial Institutions
Chiaw Boon Lee
Academos Journal · 2026-09-14
This paper develops the Governance to Evidence Responsible AI (GERA) Framework, a structured set of six auditable control domains—mandate and ownership, data and fairness, model validation, decision orchestration, human accountability, and continuous assurance—designed to translate responsible AI principles into operational controls for financial crime compliance. Drawing on MAS FEAT principles, NIST AI RMF, FATF guidelines, and international standards via integrative literature review and design-science methodology, the framework maps every governance requirement to an accountable decision right, an operational control, and evidence retention. A transaction monitoring application illustrates how institutions must be able to reconstruct and challenge both decisions to escalate and decisions not to escalate. The paper argues that responsible AI adoption requires governing the entire decision pathway, not just model accuracy, with proportional use-case classification, independent validation, versioned decision logs, and suspension triggers.
- AI policy
- Enterprise
Research
Artificial intelligence diffusion, educational capacity, and income inequality in China: evidence from structural transmission channels
Chengwei Liu, Xiong Xiaojuan, Tajul Ariffin Masron
Scientific Reports · 2026-09-14
Using Chinese provincial panel data from 2010 to 2024, this study finds that AI diffusion is positively associated with income inequality, as measured by the Theil index and the urban–rural income gap. Channel analysis reveals that AI drives asymmetric labor reallocation, amplifies industrial structural deviation, and weakens rural industrial integration—mechanisms that together widen inequality. Educational capacity partially offsets these effects, but in heterogeneous ways: basic education builds broad adaptability, higher education reduces industrial structural deviation, and vocational education aids labor adjustment and rural integration. The findings underscore the importance of targeted education investment for achieving inclusive AI diffusion, particularly in economies with regional and urban–rural disparities.
- Workforce
- AI policy
Research
From model failure to system harm: operationalizing a sociotechnical pathway for healthcare AI safety
Burhan Sebin, Irem Karaman Sebin
Frontiers in Artificial Intelligence · 2026-09-14
This paper argues that standard AI performance metrics (discrimination, calibration, accuracy, etc.) are insufficient for ensuring safety in healthcare AI systems. The authors propose a five-stage sociotechnical pathway that traces how a vulnerability propagates from upstream model risk through human-workflow mediation to downstream patient harm, treating each transition as an auditable control point with measurable indicators and accountable actors. The framework draws on postmarket reports, human-AI studies, drift analyses, and equity audits to demonstrate that hazards can emerge at multiple points beyond model output. The work is directly relevant to quality assurance and policy for healthcare AI deployment.
- Quality assurance
- AI policy
Research
Yapay Zekâ Ajanlarının Programlanabilir Ödemelerinde Kimlik Yetkilendirme ve Kontrol Katmanı
Ebru Özpolat
Zenodo (CERN European Organization for Nuclear Research) · 2026-09-14
This working paper proposes a governance framework called Know Your Agent (KYA) to address the identity and authorization gaps that arise when AI agents initiate programmable payments on behalf of people or organizations. Existing KYC and AML frameworks identify the human or legal entity behind a financial relationship but do not establish the identity, mandate, or transaction-level authority of the software agent acting for that entity. The KYA model comprises four elements—verified principal, registered agent identity, machine-enforceable mandate, and transaction-level policy evaluation—along with eleven control areas such as least-privilege authorization, behavioral monitoring, emergency revocation, and liability allocation. The paper also outlines a threat model covering risks like indirect prompt injection and unauthorized sub-agent delegation, and proposes testable hypotheses and a phased pilot framework for evaluating KYA implementations.
- AI policy
- Enterprise
Research
LARGE LANGUAGE MODELS IN MEDICATION MANAGEMENT: CLINICAL UTILITY, SAFETY ASSURANCE, AND PHARMACIST-LED GOVERNANCE
Júlia Costa Oliveira Ornelas
Nexus Science Review · 2026-09-14
This integrative narrative review examines how large language models (LLMs) can support medication management tasks such as drug information retrieval, reconciliation, and patient-facing documentation, while warning that fluency does not equal clinical reliability. The authors synthesize evidence across pharmacy practice, patient safety, and AI governance to show that published evaluations reveal substantial performance variation and serious hazards including confabulation, fabricated citations, critical omissions, and automation bias. The paper introduces the PLLAMM framework—a five-stage pharmacist-led governance structure (Govern, Map, Measure, Manage, Monitor)—concluding that current evidence supports only conditional, supervised utility for bounded tasks and does not justify autonomous medication counseling or dosing decisions.
- Quality assurance
- AI policy
Research
Yapay Zekâ Ajanlarının Programlanabilir Ödemelerinde Kimlik Yetkilendirme ve Kontrol Katmanı
Ebru Özpolat
Zenodo (CERN European Organization for Nuclear Research) · 2026-09-14
This working paper proposes 'Know Your Agent' (KYA), a governance framework designed to address the identity and authorization gaps that arise when AI agents autonomously initiate programmable payments on behalf of individuals or organizations. The authors argue that existing KYC and AML frameworks identify the human or legal entity behind a financial relationship but do not establish the identity, mandate, or operational boundaries of the software agent acting for that entity. The KYA model introduces four elements—verified principal, registered agent identity, machine-enforceable mandate, and transaction-level policy evaluation—alongside eleven institutional control areas and a threat model covering risks like prompt injection, credential compromise, and unauthorized sub-agent delegation. The framework is presented as a complementary operational design tool, not a replacement for existing regulatory obligations, and concludes with testable hypotheses and a phased pilot framework for evaluation.
- AI policy
- Enterprise