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
Artificial Intelligence Governance and Banking Regulatory Compliance: A Multiple-Case Study of Commercial Banks in Uganda
Joseph Kikomeko, Augustine Alloysius OGBE
Journal of Banking and Financial Dynamics · 2026-07-23
This qualitative multiple-case study examines how four Tier-1 commercial banks in Uganda navigate AI governance and regulatory compliance. Interviewing 24 key informants including Chief Risk Officers and IT Directors, the researchers found that despite strong technical capabilities, banks are constrained by fragmented internal governance, absent local algorithmic auditing protocols, and gaps in Bank of Uganda regulatory oversight. The study recommends that the Bank of Uganda issue explicit, risk-based AI governance guidelines and that banks establish independent algorithmic oversight committees to address these deficiencies.
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Research
Finite-Sample Coverage Audits for High-Recall Candidate Generation: Certification and Learning-Theoretic Design
M I Anthony, Kaveh Salehzadeh Nobari
arXiv (Cornell University) · 2026-07-23
This paper addresses how many labeled examples are needed to rigorously certify that a high-recall candidate generation stage (which filters items for later review or modeling) misses only a small fraction of relevant items. The authors prove that auditing only the included candidates is fundamentally insufficient—excluded items must be sampled—and establish matching minimax lower bounds showing excluded-pool auditing is rate-optimal. They then develop an exact finite-sample certification toolkit using binomial and hypergeometric inversion that can certify missed mass, convert it to recall, and select the least burdensome candidate generator meeting a missed-mass target, with all guarantees requiring pre-registration of the candidate generator and audit rule before labels are examined.
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Research
Who's responsible anyway? Contextualising governance innovation in the age of AI
Bhargavi Ganesh
ERA · 2026-07-23
This dissertation examines how AI governance frameworks address the 'accountability gap' created by AI's opacity and the many actors involved in its design, deployment, and use. Drawing on comparative historical analysis of steamboat-era regulation and 22 qualitative interviews with AI governance practitioners, the author finds that current AI ethics principles and regulations have produced limited real accountability. The research develops a conceptual framework showing how disparities in information, resources, expertise, and incentives across policymakers and stakeholders complicate responsibility negotiations, and argues that policy innovation—not just technical innovation—is essential for developing shared responsibility norms.
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Research
Mapping the Landscape of Robotic Process Automation in Education: A Systematic Literature Review
Van-Huy Chu Xuan-Lam Pham
Journal of Intelligent Decision Making and Information Science · 2026-07-23
This systematic literature review maps research on Robotic Process Automation (RPA) in education, analyzing 78 publications bibliometrically and synthesizing 33 studies in depth. The review finds that RPA can substantially enhance administrative efficiency, reduce routine workloads, and enable data-driven decision-making in educational institutions. However, successful implementation depends on addressing socio-technical challenges including governance, organizational readiness, and developing digital skills among staff. The study identifies future research directions toward intelligent and learner-centered automation in higher education.
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Research
“Rich-get-richer”? Platform attention and earnings inequality using Patreon earnings data
Ilan Strauss, Jangho Yang, Mariana Mazzucato
Industrial and Corporate Change · 2026-07-23
This study uses Patreon earnings data across major platforms (YouTube, Twitch, Instagram, etc.) to examine whether content creator income follows 'rich-get-richer' dynamics. The authors fit power-law distributions to earnings and find a Pareto exponent near 2—closer to concentrated capital income than labor income—indicating high inequality. Platforms with more concentrated earnings also have lower mean and median creator pay, hollowing out a creator 'middle class,' and this concentration has increased from 2018 to 2024, consistent with algorithmic recommendations amplifying winner-take-most dynamics. The findings raise concerns about how platform algorithms shape economic opportunity for independent content workers.
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Research
How do datasets, developers, and models affect biases in a low-resourced language?: The Case of the Bengali Language
Dipto Das, Shion Guha, Bryan Semaan
arXiv · 2026-07-23
This paper empirically audits Bengali sentiment analysis (BSA) models built on mBERT and BanglaBERT, fine-tuned on all Bengali sentiment analysis datasets from Google Dataset Search, to measure gender, religion, and nationality-based biases. The study finds that BSA models exhibit identity-based biases across these categories even when inputs share similar semantic content and structure, and that inconsistencies arise when pre-trained models are combined with datasets created by developers from diverse demographic backgrounds. The findings challenge common recommendations—such as using language-specific or multilingual models—as sufficient remedies for bias in low-resource language contexts. The authors connect their results to broader debates on epistemic injustice, AI alignment, and methodological choices in algorithmic auditing.
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Research
Execution and Evaluation: A New Occupational Measure and Long-Run Employment Gradients
Li Gan
arXiv (Cornell University) · 2026-07-23
This paper introduces a new occupation-level measure that distinguishes between 'execution' tasks (producing output) and 'evaluation' tasks (judging correctness), arguing that AI automates execution more readily than evaluation. Scoring all 19,265 O*NET task statements, the author finds that employment growth has been consistently lower in execution-heavy white-collar occupations since 2012—a secular trend rather than a distinctly AI-era phenomenon. The AI-capability gradient does steepen after 2022, but the paper cautions this is a correlation, not a proven causal effect. The work establishes a reproducible measurement framework and a chronology for tracking AI's occupational footprint.
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Research
A conceptual framework for measuring AI health equity
Basile Njei, Ulrick Sidney Kanmounye, Luchuo Engelbert Bain et al.
International Journal for Equity in Health · 2026-07-23
This paper proposes the AI in Healthcare Equity Index (AIHEI), a composite framework for measuring equity in health AI systems across five domains: data representation, algorithmic fairness, transparency and explainability, governance and oversight, and community impact and benefit sharing. The index would generate a standardized score to enable comparisons across technologies and inform regulation, procurement, and funding decisions, with particular attention to underserved populations in low- and middle-income countries. The authors argue that without such a tool, AI risks reinforcing structural health disparities, and call for pilots across diverse settings to test feasibility and validity.
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Research
Sustainable Automation in Financial Services: Evaluating Chatbot Efficiency, Equity, and Experience in Costa Rica’s Digital Transformation
Tom Okot, Yirlany Melissa Salas Jiménez
Studia Universitatis „Vasile Goldis” Arad – Economics Series · 2026-07-23
This mixed-methods study examined 12 months of chatbot deployment data at a Costa Rican financial contact center, finding a 43.7% reduction in Average Handling Time and a 26.6% decline in indirect operational costs. However, Average Speed of Answer did not significantly improve, and 90.4% of users still preferred human agents, suggesting chatbot gains in efficiency do not automatically translate to better perceived service quality. Structural equation modeling showed customer satisfaction was influenced indirectly through cost and time efficiency rather than through direct chatbot interaction. The authors recommend hybrid AI-human models with emotional responsiveness and escalation protocols as a scalable framework for AI adoption in human-centered service environments.
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Research
The VIBE-HI framework: a conceptual model for evaluating vibe coding appropriateness, quality, and safety in health informatics
Ahmed Alqheedan, Saleh Alzughaibi
Frontiers in Artificial Intelligence · 2026-07-23
This paper introduces VIBE-HI, a conceptual governance framework designed to evaluate the appropriateness, quality, and safety of 'vibe coding'—generating software via natural-language prompts to large language models without reviewing the underlying code—specifically within health informatics contexts. The framework organizes governance into three sequential layers: risk and role stratification across four tiers (Green, Yellow, Orange, Red), quality and validation constructs extending ISO/IEC 25010:2023, and compliance mapping to HIPAA, IEC 62304, FDA SaMD criteria, and the EU AI Act. The authors identify 'comprehension abdication'—the structural surrender of code understanding to a generative system—as the core sociotechnical hazard unique to vibe coding, distinguishing it from prior AI-assisted development. The paper argues that risk-stratified governance is urgently needed as clinical adoption of vibe coding is already outpacing the field's capacity to assess it, and proposes a modified-Delphi consensus study as the next validation step.
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Research
Rethinking Technology Acceptance in Automation Contexts: Evidence from Robotic Process Automation Adoption in Vietnamese Higher Education Institutions
Van-Huy Chu Xuan-Lam Pham
Journal of Intelligent Decision Making and Information Science · 2026-07-23
This study investigates why academic and administrative staff at Vietnamese public universities adopt or resist Robotic Process Automation (RPA), extending the standard UTAUT technology acceptance model to include automation anxiety as a barrier. Surveying 200 staff and using PLS-SEM, the study finds that performance expectancy is the strongest driver of adoption intent (β = 0.683), facilitating conditions also matter (β = 0.240), and automation anxiety exerts a significant negative effect (β = -0.388), together explaining 77.2% of variance in behavioral intention. The findings suggest that RPA uptake in higher education hinges on demonstrating clear performance benefits while actively addressing staff fears about automation. Practically, institutions should pair RPA rollouts with communication strategies that highlight productivity gains and mitigate job-related anxieties.
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Research
Artificial Intelligence and Generative Models in Hepatology: From Large Language Models to Digital Pathology in Liver Disease Diagnosis and Treatment
Nana Peng, Mary Yue Wang, Sherlot Juan Song et al.
Clinical and Molecular Hepatology · 2026-07-23
This narrative review examines how AI—including large language models, multimodal foundation models, and agentic AI—is being applied across hepatology subspecialties such as fatty liver disease, hepatitis B, cirrhosis, hepatocellular carcinoma, and liver transplantation. LLMs show promise for converting clinical notes to structured data, summarizing electronic health records, and retrieving guideline-based information, while discriminative AI has enabled more reproducible histologic scoring in digital pathology. However, the authors note that most generative AI applications remain at proof-of-concept stage and carry risks including hallucination, automation bias, and inequities from underrepresented patient subgroups. Rigorous prospective validation with human-in-the-loop oversight is required before clinical integration, and the authors call for lifecycle governance, federated evaluation, and continuous monitoring for performance and equity.
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Research
Bangladesh AI Readiness: Gaps in Curriculum, Infrastructure, and Governance
Sharifa Sultana, Rupali Tasnim Samad, Mehzabin Haque et al.
arXiv · 2026-07-23
This qualitative study of 35 university programs and 59 stakeholder interviews in Bangladesh reconceptualizes AI readiness as a sociotechnical condition shaped by infrastructure, human capacity, and curricular governance. The research finds that GPU scarcity, limited faculty upskilling, opaque mentorship networks, gender disparities, and near-absent Responsible AI instruction collectively constrain institutional capacity. Using Science and Technology Studies concepts, the authors show these deficits arise from layered bureaucratic systems and postcolonial dynamics that prioritize global labor alignment over local innovation. The paper offers design and policy pathways for building more equitable AI education ecosystems in Global South contexts.
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Research
Current challenges for global equity related to the implementation of artificial intelligence in pediatric imaging
Rutger A. J. Nievelstein, AN Gupta, Joanna Kasznia-Brown et al.
Pediatric Radiology · 2026-07-23
This paper from the World Federation of Pediatric Imaging identifies major barriers to equitable global adoption of AI in pediatric radiology, including data bias, infrastructure gaps, regulatory and ethical shortfalls, workforce training deficiencies, language barriers, and cost issues. The authors argue that without deliberate intervention these challenges will widen existing health disparities for children worldwide. They propose a time-sequenced, equity-focused roadmap that assigns practical actions and responsibilities to guide fairer implementation across diverse health systems.
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Research
From Digital Inclusion to Digital Resilience: A Systematic Review of AI-Mediated Informal Micro-Enterprise Systems in Africa
Ismail Sheik, Jobo Dubihlela, Bibi Zaheenah Chummun
Systems · 2026-07-23
This systematic review synthesizes evidence from 60 peer-reviewed articles on how AI-mediated digital tools—including mobile money, platform payments, algorithmic credit scoring, and app-based logistics—affect informal micro-enterprises in Africa. The findings show that while digitalisation can expand market access, reduce cash-handling risks, and strengthen household resilience, the same systems can intensify vulnerability through opaque algorithmic scoring, unexplained account freezes, exclusionary verification, and weak dispute resolution. The authors argue that informal enterprise digitalisation is fundamentally a socio-technical governance challenge, not merely a technology adoption issue, and propose a governance-and-risk framework identifying minimum policy protections such as transparent fees, explainable restrictions, human appeal channels, and data-use consent. The review concludes that sustainable digital inclusion depends on the fairness, transparency, recoverability, and accountability of the systems through which traders participate.
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Research
The gift of green: Does intelligent manufacturing improve corporate environmental performance?
Shuang Zhao, Changgao Cheng, Feng Hu et al.
Humanities and Social Sciences Communications · 2026-07-23
This paper empirically examines how intelligent manufacturing pilot initiatives in China affect corporate environmental performance, finding that pilot enterprises achieved ESG-E scores approximately 3.46% higher than non-pilot firms. The effect operates primarily through green innovation promotion and increased market attention. Heterogeneity analysis shows that smaller firms and those outside high-pollution or high-tech industries gain the greatest environmental benefits, offering practical guidance for green transformation policy.
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Research
AI-Supported Adaptive Learning in Vocational Beauty Education: Effects on Practical Competence and Entrepreneurial Readiness
Trisnani Widowati
Journal of Intelligent Decision Making and Information Science · 2026-07-23
This quasi-experimental study tested an AI-supported adaptive learning system in vocational beauty education, comparing students who used the personalized system against a control group receiving conventional instruction. The experimental group showed significantly higher practical competence and entrepreneurial readiness, with moderate-to-large effect sizes (Hedges' g ≈ 0.70 and 0.72 respectively). The findings suggest that personalizing learning activities and assessments based on individual competency levels produces measurable gains in both technical skills and entrepreneurial preparedness in vocational settings.
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Research
EAGF: a four-pillar ethical AI governance framework for trustworthy cybersecurity in 5G renewable energy IoT systems
Salman Jan, Ali Akarma, Toqeer Ali Syed et al.
Scientific Reports · 2026-07-23
This paper introduces EAGF, a four-pillar Ethical AI Governance Framework that maps EU AI Act requirements—transparency, fairness, privacy, and accountability—onto computable engineering metrics unified within a single AI training-and-deployment lifecycle. Evaluated on both a biometric image dataset and a real-world industrial IoT intrusion-detection benchmark, EAGF achieved Trust Index gains of +38.97% and +69.3% respectively, with substantial improvements in fairness parity and privacy, at negligible inference overhead. The results demonstrate that joint multi-pillar governance outperforms model-level-only approaches and that accountability infrastructure contributes a large, quantified share of total governance gains. This work matters for enterprise and policy contexts because it operationalizes AI Act compliance requirements into measurable metrics applicable to 5G and IoT cybersecurity systems.
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Research
Assessing The Relationship Between AI-Assisted Report Generation and Employee Productivity, Decision-Making, And Well-Being Among NIA-UPRIIS Employees
Ma. Andrea I. Balagtas, Christopher Ladignon, Prof. Noel Florencondia
Iconic Research and Engineering Journals · 2026-07-23
This study surveyed 300 employees at a Philippine government irrigation agency (NIA-UPRIIS) to assess how AI-assisted report generation relates to productivity, decision-making, and well-being. Using Pearson correlation, the researchers found statistically significant positive relationships between AI tool use and all three outcomes (r = 0.373, 0.307, and 0.351 respectively, all p < 0.01). The findings suggest that AI adoption in government workplaces is associated with better performance and employee well-being, and the authors recommend pairing AI adoption with training, ethical guidelines, and organizational support.
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Research
Exposing the Wizard of Oz: Transparency and Testing of Artificial Intelligence Systems
Henry H. Perritt Jr.
arXiv · 2026-07-23
This policy paper argues that calls for AI regulation—particularly of generative AI systems like ChatGPT—are often ill-informed and premature. The author contends that transparency requirements are preferable to command-and-control regulation, and distinguishes between harmful transparency mandates (forcing disclosure of proprietary model internals) and meritorious ones (disclosing training data scope, disclosure of AI use, result quality, and access to human appeals). The paper urges regulators to observe real-world deployment before legislating based on hypothetical harms.
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Research
Artificial intelligence in psychiatry: clinical applications, limitations, and ethical challenges
Pedro Morgado
Frontiers in Behavioral Neuroscience · 2026-07-23
This perspective article reviews AI applications in psychiatry—including diagnosis, risk prediction, digital phenotyping, and treatment personalization—while highlighting serious methodological and ethical limitations. The authors note that most neuroimaging-based AI models carry high bias risk, external validation is rare, and real-world clinical impact is largely unproven. The paper raises urgent concerns about sensitive mental health data being controlled by large technology corporations, risks of encoding culturally contingent norms as medical standards, and the need for patient-centered data governance frameworks. The psychiatric community is urged to take an active governance role rather than allow human suffering to be reduced to a monetizable data stream.
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Research
Cognitive Debt and the Regulatory Blind Spot: Bridging Neurocognitive Evidence, Practitioner Observation and the EU AI Act on AI in Education
Alessandro Ricardo Gomes Ferreira, Rizzia Nunes Nunes Costa
European Journal of Risk Regulation · 2026-07-23
This paper argues that the EU AI Act's human oversight requirements (Article 14) contain a 'cognitive blind spot': they regulate risks at a single point in time but ignore the long-term erosion of the cognitive capacities that meaningful oversight requires. Drawing on neurocognitive research, the authors identify four mechanisms—cognitive offloading, atrophy through disuse, transfer-appropriate processing failure, and engagement asymmetry—through which sustained AI use in education can accumulate 'cognitive debt.' Longitudinal practitioner observations from software-engineering management roles before and after LLM adoption are presented as real-world corroboration of these experimental findings. The authors call on institutions, providers, and regulators to address this gap before high-risk AI obligations under the Act enter into force.
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Research
Deep technologies for responsible gambling: A narrative review of policies and implementation strategies
Leonor G. Cardoso, Beatriz Barroso, Eduardo Rocha Dias et al.
Journal of Behavioral Addictions · 2026-07-23
This narrative review maps how deep technologies—AI, blockchain, and behavioural analytics—intersect with responsible gambling regulations across multiple jurisdictions including the EU, UK, Malta, and non-European countries. The review finds very few formal legal instruments explicitly governing these technologies in gambling contexts, though the EU AI Act represents a pioneering binding step for high-risk AI systems. Beyond Europe, regulatory frameworks are largely non-binding or absent, with operators acting voluntarily. The authors call for proactive, ethics-based governance with enforceable, transparent, and harmonised policies to close persistent gaps in legal oversight and consumer protection.
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Research
Governing Adaptive News Curation: Sequential Optimization, Cumulative Exposure Allocation, and Societal Accountability
Dan Valeriu Voinea
Social Sciences · 2026-07-23
This conceptual paper analyzes how adaptive AI systems curate news—ranking, sequencing, moderating, and generating content—and proposes a framework for evaluating their societal effects over time. It introduces 'cumulative exposure allocation' as a measurable construct capturing how visibility is distributed across sources, topics, and population groups, with proposed metrics for concentration, breadth, and disparity. The paper maps each curation function to the actors who control it and the oversight bodies responsible, using EU and US law as illustrations. The analysis argues that accountability should focus on long-run visibility patterns produced by algorithmic policy rather than isolated outputs or short-term engagement metrics.
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Research
STUDY REPORT Project Business in the Age of AI: Future Competences, Changing Roles and Development Needs
Reinhard Wagner, Domagoj Mihajljević, Adam Galgenmüller
arXiv · 2026-07-23
This study report examines how AI is reshaping project management roles and required competencies, finding that respondents estimate AI could take over 51.6% of traditional project management tasks by 2030 and 63.5% by 2035. Rather than displacing project professionals entirely, the report finds their work will shift away from routine administration toward evaluating AI outputs, interpreting complex situations, stakeholder coordination, and decision-making judgment. The report is directed at project professionals, PMOs, educators, and organizations, offering guidance on how to proactively prepare workforces for human-AI collaboration in project environments.
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