{
  "count": 50,
  "items": [
    {
      "id": 19553,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1876322",
      "title": "Meditation styles are highly discriminable from EEG at the subject level with limited generalization across the population: a machine-learning study",
      "summary": "Meditation has been associated with improvements in attention, emotional regulation, and mental wellbeing, motivating increasing interest in objective methods for assessing meditative states. In this study, we investigate whether EEG-based machine learning can reliably distinguish between multiple meditation styles and mind-wandering states. EEG data were recorded from experienced meditators performing three meditation styles, Shamatha, Vipassana, and Metta, together with an eyes-closed mind-wan",
      "authors": "Saqib Hayat",
      "category": "research",
      "topics": "regulation,finance-investment",
      "published_at": "2026-08-14T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/19553"
    },
    {
      "id": 19554,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1817529",
      "title": "Reassessing demographic bias in face attribute classification: a statistically grounded multi-model evaluation on FairFace and UTKFace",
      "summary": "Face analysis systems are widely used in security, authentication, and public-sector applications; however, demographic bias and the statistical reliability of reported performance remain key concerns. Many studies rely on aggregate accuracy without quantifying subgroup disparities or uncertainty, potentially overstating model fairness. This study presents a statistically grounded evaluation of demographic bias in face attribute classification across three representative architectures, ResNet50,",
      "authors": "Andisani Nemavhola",
      "category": "research",
      "topics": "bias-fairness",
      "published_at": "2026-08-14T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/19554"
    },
    {
      "id": 19555,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1823263",
      "title": "Explainable artificial intelligence in accounting and financial auditing: a systematic review",
      "summary": "Explainable Artificial Intelligence (XAI) has emerged as a response to the need to understand and make transparent the decisions of machine learning models, particularly in sensitive contexts such as accounting and financial auditing. In this domain, XAI enables the interpretation of results generated by automated systems applied to fraud detection, risk management, financial analysis, and regulatory compliance, thereby strengthening the trust of auditors and regulators. The objective of this st",
      "authors": "Iván Patricio Arias-González",
      "category": "research",
      "topics": "regulation,transparency",
      "published_at": "2026-08-14T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/19555"
    },
    {
      "id": 19556,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1800407",
      "title": "LATTICE: a governance-first architecture for authorized autonomous AI operations",
      "summary": "Deploying autonomous AI agents in high-consequence operational environments requires organizational authorization, yet few frameworks provide end-to-end, testable governance mechanisms suitable for such authorization decisions. This paper introduces LATTICE (Layered Agentic Triad Topology for Intelligent Coordinated Execution), a governance-first architecture that reframes the authorization question from “do we trust this AI?” to “do we trust this architecture?” The latter question is answerable",
      "authors": "Elias Calboreanu",
      "category": "research",
      "topics": "regulation,agents-autonomy,environment",
      "published_at": "2026-08-14T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/19556"
    },
    {
      "id": 19557,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1875232",
      "title": "Automated detection and counting of redbanded stink bugs in soybean using an improved computer vision model",
      "summary": "The redbanded stink bug (RBSB), Piezodorus guildinii, is a major economic pest of soybean, with feeding damage that leads to significant yield losses and increased reliance on pesticide applications. Current management practices depend on manual identification and repeated field counting, which are labor-intensive, time-consuming, and prone to human error, particularly across large production areas. To address these limitations, this study evaluated the potential of computer vision models to aut",
      "authors": "Saurav Upadhyaya",
      "category": "research",
      "topics": "jobs-economy",
      "published_at": "2026-08-14T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/19557"
    },
    {
      "id": 18852,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1836646",
      "title": "Recent advancements and future prospects on AI-integrated sensing techniques for non-invasive chronic kidney disease diagnosis: a review",
      "summary": "Chronic Kidney Disease (CKD) has emerged as a major public health concern worldwide, and most patients with CKD are asymptomatic until the later stages, causing growing morbidity and mortality. Diabetes and hypertension are the main causative factors for the development of CKD, damaging the renal microcirculation system. In addition, the impact of Acute Kidney Injuries (AKI) may result in the recovery or progression to either CKD or renal failure. The conventional techniques for diagnosis, such",
      "authors": "Suchetha Manikandan",
      "category": "research",
      "topics": "healthcare",
      "published_at": "2026-08-13T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/18852"
    },
    {
      "id": 18476,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1784973",
      "title": "What really happens when a dev vibes with the code? An empirical study on LLM behavioral divergence in response to expressive code comments",
      "summary": "IntroductionWe investigate how expressive inline code comments written in various developer styles, functional to progressively poetic, philosophical, and misleading, affect large language model (LLM) behavior during code optimization.MethodsIn this pilot study, we used a controlledmerge sort implementation across five stylistic variants and evaluated GPT-5 and Claude Opus 4.1 under standardized console prompts, isolating the effect of embedded comment semiotic variation. Seven expert developers",
      "authors": "Angela N. Johnson",
      "category": "research",
      "topics": "finance-investment",
      "published_at": "2026-08-11T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/18476"
    },
    {
      "id": 18477,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1883357",
      "title": "Application of dimensionality reduction and clustering techniques for the analysis of Carrion's disease cases in the period 2000–2024",
      "summary": "The heterogeneous geographic distribution and the complex dynamics of Carrion's disease challenge conventional epidemiological surveillance in Peru. To address this, this study applied unsupervised machine learning to 43,534 national records (2000–2024). Following a rigorous data cleaning process—which resolved duplicate records, missing information, and outliers using Tukey's interquartile range (IQR)—the dimensionality reduction approaches MCA and FAMD coupled with the K-Means algorithm were e",
      "authors": "Moisés Evangelista Gamarra",
      "category": "research",
      "topics": "privacy-surveillance",
      "published_at": "2026-08-11T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/18477"
    },
    {
      "id": 18478,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1886896",
      "title": "Predicting influenza in the post-COVID era: assessing LSTM, GRU, and transformer robustness to covariate shift",
      "summary": "Forecasting influenza has become increasingly challenging due to post-COVID disruptions in seasonality and strain circulation. This work compares the performance of Long Short Term Memory Networks (LSTM), Gated Recurrent Unit (GRU), and transformer models in forecasting influenza spread using multivariate epidemiological and environmental data, with a focus on robustness under post-COVID non-stationarity. We compare LSTM, GRU, and transformer architectures within a multivariate deep learning fra",
      "authors": "Atiqa Naeem Alam Din",
      "category": "research",
      "topics": "environment",
      "published_at": "2026-08-11T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/18478"
    },
    {
      "id": 18479,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1884843",
      "title": "Automated evaluation of dental cavity preparation quality using deep learning and anatomically informed geometric analysis",
      "summary": "BackgroundThe quality of cavity preparation critically influences the longevity and success of restorative dental treatments. Current assessment methods remain largely subjective, relying on visual inspection and examiner judgment, which are prone to variability and limited reproducibility. Although three-dimensional (3D) imaging enables quantitative evaluation, its routine use in clinical and educational settings is limited by cost, accessibility, and workflow complexity.ObjectiveThis study aim",
      "authors": "Abdullah F. Alshammari",
      "category": "research",
      "topics": "healthcare",
      "published_at": "2026-08-11T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/18479"
    },
    {
      "id": 18074,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1909177",
      "title": "Deep learning and hybrid architectures for atypical and complex bone fracture diagnosis: a systematic review of performance and clinical validity",
      "summary": "Artificial intelligence (AI) is reshaping fracture diagnosis in medical imaging. Despite these advances, accurately identifying atypical fractures (such as stress or pathological fractures) and complex fractures (including comminuted and pelvic fractures) remains a significant clinical challenge. This systematic review evaluates the current evidence on AI models, including advanced architectures, for detecting, classifying, and segmenting atypical and complex bone fractures in humans. A total of",
      "authors": "Fatma Atitallah",
      "category": "research",
      "topics": "healthcare",
      "published_at": "2026-08-10T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/18074"
    },
    {
      "id": 18075,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1885655",
      "title": "Use of artificial intelligence in building personal branding and intercultural leadership",
      "summary": "The integration of AI tools reshaping how professionals learn, build reputation, and project their value in digitally and culturally diverse environments. Therefore, this study aims to analyze the relationships among AI tool use, personal branding, and leadership in intercultural contexts. A quantitative, non-experimental, cross-sectional, and correlational study was conducted with 169 university graduates from Peru, Ecuador, and Mexico. Data were collected using a Likert-scale questionnaire and",
      "authors": "Omer Cruz Caro",
      "category": "research",
      "topics": "environment",
      "published_at": "2026-08-10T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/18075"
    },
    {
      "id": 18076,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1895239",
      "title": "GA-AFedOD: gradient-aligned active federated learning for resource-aware object detection in edge industrial IoT",
      "summary": "Visual object detection is essential for defect inspection and process monitoring in edge-deployed Industrial Internet of Things (IIoT). Yet, training accurate detectors across distributed factories faces stringent constraints on data privacy, annotation budgets, and uplink communication. Standard federated learning (FL) preserves locality but often wastes labeling resources on redundant frames and overlooks detection-specific gradient alignment when scheduling clients. To bridge this gap, we pr",
      "authors": "Zepeng Wang",
      "category": "research",
      "topics": "safety-alignment,privacy-surveillance",
      "published_at": "2026-08-10T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/18076"
    },
    {
      "id": 18077,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1813948",
      "title": "Scanner-agnostic MRI harmonization via SSIM-guided disentanglement",
      "summary": "IntroductionThe variability introduced by differences in MRI scanner models, acquisition protocols, and imaging sites hinders consistent analysis and generalizability across multicenter studies.MethodsWe present a novel image-based harmonization framework for 3D T1-weighted brain MRI, which disentangles anatomical content from scanner- and site-specific variations. The model incorporates a differentiable loss based on the Structural Similarity Index Measure (SSIM) to preserve biologically meanin",
      "authors": "Luca Caldera",
      "category": "research",
      "topics": "biotech",
      "published_at": "2026-08-10T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/18077"
    },
    {
      "id": 18078,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1869972",
      "title": "Faithful or evasive? An empirical study on translation norm preferences of Chinese and American LLMs in Chinese official political and policy discourse",
      "summary": "IntroductionLarge language models now handle a share of cross-lingual political translation, but no study has directly measured whether they follow stable normative preferences when doing so.MethodsWe target Chinese-to-English translation of Chinese political and policy terms embedded in authentic official discourse. Mapping 52 publications across translation theory, LLM empirics, and AI alignment yields a five-dimensional Translation Norm Orientations (TNO) framework: Faithfulness (FN), Fluency",
      "authors": "Xinyu Zhu",
      "category": "research",
      "topics": "regulation,safety-alignment",
      "published_at": "2026-08-10T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/18078"
    },
    {
      "id": 18079,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1871363",
      "title": "Generative AI as a cognitive co-learner: a developmental framework for AI literacy in health sciences education",
      "summary": "Generative artificial intelligence (AI), especially large language models, is playing an expanding role in shaping learning within health sciences education. Current discussions often focus on efficiency or academic integrity, with less attention to how learners engage with AI across evolving cognitive and developmental stages. This Perspective conceptualizes generative AI as a cognitive co-learner, an interactive system that supports idea generation, organization, and reasoning while requiring",
      "authors": "Myo Zin Oo",
      "category": "research",
      "topics": "healthcare,children-education",
      "published_at": "2026-08-10T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/18079"
    },
    {
      "id": 17495,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1898153",
      "title": "Quantifying the imputation paradox and XAI inconsistency in multi-source diabetes prediction: a 353,680-record leakage-free stacking ensemble with dynamic routing architecture",
      "summary": "Diabetes affects 537 million adults globally, a figure projected to reach 783 million by 2045. Despite over 4,200 ML prediction studies, clinical translation remains hindered by an over-reliance on benchmark datasets, unmeasured information costs of multi-source fusion, and untested XAI convergence assumptions. We address these issues by evaluating 353,680 records (from 455,446 candidates) across a Clinical-Biomarker Set (CBS) and a Lifestyle-Survey Set (LSS) using a strict leakage-free protocol",
      "authors": "M. Nanda Kishore",
      "category": "research",
      "topics": "healthcare",
      "published_at": "2026-08-07T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/17495"
    },
    {
      "id": 17496,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1827727",
      "title": "Zero-shot multimodal pain estimation via synthetic pain simulation and domain-invariant learning",
      "summary": "IntroductionPain assessment in non-communicative populations–particularly neonates and cognitively impaired patients–remains a critical clinical challenge, as current automated methods require labeled pain datasets that are both ethically problematic and scarce for vulnerable populations.MethodsWe propose a framework trained on zero labeled real pain examples from the target population, combining synthetic pain simulation with unsupervised domain adaptation. Using latent diffusion models, we gen",
      "authors": "Oussama El Othmani",
      "category": "research",
      "topics": "healthcare",
      "published_at": "2026-08-07T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/17496"
    },
    {
      "id": 17497,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1867966",
      "title": "Automatic transformation of Kazakh text into sign language glosses using multilingual transformer-based models",
      "summary": "This study investigates the automatic transformation of Kazakh text into sign language glosses (Text-to-Gloss) using multilingual transformer-based models with emphasis on preserving morphological structure in a low-resource agglutinative language framework. Given the scarcity of high-quality intermediate representations for Kazakh Sign Language, a methodology for corpus formation was developed, resulting in a specialized dataset of 11 190 unique text−gloss pairs sourced from educational materia",
      "authors": "Nurzada Amangeldy",
      "category": "research",
      "topics": "finance-investment",
      "published_at": "2026-08-07T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/17497"
    },
    {
      "id": 17143,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1881543",
      "title": "Generative AI-enhanced synthetic X-ray augmentation with gradient-based selection for battery detection in WEEE",
      "summary": "Automated detection of batteries in Waste Electrical and Electronic Equipment (WEEE) using X-ray imaging is critical for safe recycling, yet collecting large annotated real-world datasets remains prohibitively expensive and hazardous. This paper proposes a three-stage synthetic data pipeline to improve battery detection under limited labeled data conditions. First, dual-energy X-ray images are generated using physics-based ray-casting in Blender with automatic pixel-level annotation. Second, the",
      "authors": "Farhan Mahmood",
      "category": "research",
      "topics": "environment",
      "published_at": "2026-08-06T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/17143"
    },
    {
      "id": 17144,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1882547",
      "title": "Towards cross-center head and neck cancer detection: a multi-level domain alignment exploration",
      "summary": "IntroductionDeep learning models for head and neck cancer (HNC) detection from computed tomography (CT) hold significant promise for improving early detection—a critical priority given that 5-year survival drops from 84% for localized disease to 39% for metastatic cases. However, robust cross-center deployment remains challenging because scanner vendors, acquisition protocols, reconstruction Q15 kernels, and patient populations vary across hospitals. To address this challenge, we propose MDA-Net",
      "authors": "Jiaqi Zhao",
      "category": "research",
      "topics": "safety-alignment,healthcare",
      "published_at": "2026-08-06T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/17144"
    },
    {
      "id": 17145,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1927993",
      "title": "Digital twin–supported behavioral intention in mothers of young children to prevent childhood obesity: a large language model–based intervention study",
      "summary": "IntroductionChildhood obesity is a major determinant of lifelong non-communicable disease risk, yet early intervention may modify this trajectory. Digital twins and large language models may provide a scalable means of translating individualized risk prediction into understandable and motivating lifestyle guidance.MethodsWe developed a precision-preventive intervention integrating a childhood-overweight digital twin with an empathic, supervised-fine-tuned open-source nutrition-guidance large lan",
      "authors": "Kenji Nakamura",
      "category": "research",
      "topics": "children-education",
      "published_at": "2026-08-06T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/17145"
    },
    {
      "id": 17146,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1891883",
      "title": "RN-D3: detection, differentiation, and delineation of radiation necrosis on post-contrast MRI",
      "summary": "PurposeRadiation necrosis (RN) is an important complication of radiation therapy (RT) and is challenging to assess radiographically because lesions are prone to inconsistent delineation, often necessitating intracranial surgery to obtain a diagnosis. This study evaluated how established deep learning segmentation models can be retrained on an institutional RN cohort, assessed external generalization, and integrated the best components into RN-D3, an end-to-end pipeline for RN detection, differen",
      "authors": "Patrick Salome",
      "category": "research",
      "topics": "healthcare",
      "published_at": "2026-08-06T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/17146"
    },
    {
      "id": 17147,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1873938",
      "title": "A survey of transformer-based architectures in medical image analysis: models, applications, and challenges",
      "summary": "Transformer-based architectures have become central to medical image analysis, yet their practical value remains difficult to assess because studies vary widely in tasks, datasets, validation protocols, baselines, and reporting quality. This survey critically reviews recent transformer-based, hybrid, foundation, and transformer-alternative models across segmentation, classification, reconstruction, and image registration. A total of 128 studies published between 2021 and 2026 are organized using",
      "authors": "Sam Ansari",
      "category": "research",
      "topics": "healthcare",
      "published_at": "2026-08-06T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/17147"
    },
    {
      "id": 17148,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1856630",
      "title": "Federated spatio-temporal graph neural network for privacy-preserving vehicle trajectory prediction in autonomous driving",
      "summary": "Accurate vehicle trajectory prediction plays a vital role in autonomous driving and intelligent transport systems. Deep learning models like LSTM, CNN, GNN, etc., have shown remarkable performance but often operate in a centralized setting, aggregating raw trajectory data at the server. Furthermore, the majority of models focus on either spatial or temporal features alone, but overlook the information that can be obtained by combining spatio-temporal features. This leads to major privacy concern",
      "authors": "Aditi Joshi",
      "category": "research",
      "topics": "privacy-surveillance",
      "published_at": "2026-08-06T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/17148"
    },
    {
      "id": 17149,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1877344",
      "title": "Future-proofing agricultural research: FAIR principles for agriculture AI agents (FAIR4AG2)",
      "summary": "The rapid shift of large language models from conversational use to agentic reasoning is changing how scientific outputs must be structured for machine consumption. Agriculture stands to gain the most from this transition but currently has the least of the centralized, machine-ready infrastructure that biomedicine has built over decades. Agricultural knowledge remains dispersed across peer-reviewed journals, extension bulletins, technical reports, and multimedia field demonstrations, with associ",
      "authors": "Chenhao Qian",
      "category": "research",
      "topics": "agents-autonomy",
      "published_at": "2026-08-06T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/17149"
    },
    {
      "id": 16717,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1872217",
      "title": "EdgeLane-SEG: an energy-efficient embedded edge AI framework for real-time road marking and lane lines detection with instance segmentation in ADAS and autonomous driving",
      "summary": "ObjectiveAccurate and energy-efficient perception of road-surface markings is essential for Advanced Driver Assistance Systems (ADAS) and autonomous driving, particularly under real-time embedded constraints. This study proposes EdgeLane-SEG, a unified framework designed to achieve high instance segmentation accuracy while maintaining low computational cost and power consumption on resource-constrained edge platforms.MethodsThe proposed framework integrates two single-stage models, YOLO11-SEG an",
      "authors": "Mohammed Chaman",
      "category": "research",
      "topics": "environment",
      "published_at": "2026-08-05T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/16717"
    },
    {
      "id": 16718,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1892739",
      "title": "AdaK: adaptive KV cache budget estimation framework for analyzing long-context large language model inference",
      "summary": "IntroductionThe deployment of LLMs on resource-constrained hardware is hindered by the memory-intensive KV Cache mechanism.MethodsWe propose AdaK, an adaptive KV cache budget estimation framework with three strategies: entropy-based thresholding, task-aware lookup table, and a lightweight policy network.ResultsAdaK reveals estimated KV cache reductions of up to 17.9% relative to fixed-k = 2048 baselines across 16 settings on Qwen3-4B, Qwen3-8B, and Mistral-7B.DiscussionAdaK's decoupled design en",
      "authors": "Tianjun Shao",
      "category": "research",
      "topics": "regulation",
      "published_at": "2026-08-05T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/16718"
    },
    {
      "id": 16719,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1880070",
      "title": "A biologically structured hierarchical vision transformer-CNN framework for robust tomato leaf disease classification",
      "summary": "Precise and reliable diagnosis of leaf diseases in tomato is essential for enhancing crop cultivation and minimizing agricultural losses. While deep learning models have performed well on benchmark datasets, the majority of present techniques rely on flat multi-class classification, which predicts all disease categories simultaneously. Such formulations promotes inter-class confusion, particularly when biologically different diseases with similar visual symptoms are learned within a same model.",
      "authors": "Harshinisree Gunasekaran",
      "category": "research",
      "topics": "healthcare,biotech",
      "published_at": "2026-08-05T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/16719"
    },
    {
      "id": 16720,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1863919",
      "title": "Cyber-resilience drivers in digital business networks: an expert-based prioritization using dual-gray DEMATEL and adaptive SWARA",
      "summary": "IntroductionDigital business networks are increasingly exposed to complex cyber threats, making the identification and prioritization of cyber resilience drivers essential for organizational stability. This study aims to identify, structure, and prioritize key cyber resilience drivers relevant to digital business network environments.MethodsTo achieve this objective, an expert-based multi-criteria decision-making framework was developed by integrating Dual-Grey DEMATEL and Adaptive SWARA. Dual-G",
      "authors": "Reem Aljuaidi",
      "category": "research",
      "topics": "military-security,environment",
      "published_at": "2026-08-05T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/16720"
    },
    {
      "id": 16721,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1854179",
      "title": "Neuro-semantic graph fusion for explainable depression risk trajectory mapping using a graph-enhanced RoBERTa framework",
      "summary": "IntroductionEarly identification of depression through social media analytics is frequently compromised by linguistic ambiguities, contextual complexities, and the opaque nature of conventional deep learning architectures. This study proposes Graph-RoBERTa-CL, a hybrid neuro-semantic framework that explicitly integrates three synergistic components: (i) RoBERTa-based Transformer semantic encoding for deep contextual representation, (ii) Graph Attention Networks (GAT) for relational contextual ag",
      "authors": "M. Karthiga",
      "category": "research",
      "topics": "transparency",
      "published_at": "2026-08-05T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/16721"
    },
    {
      "id": 15712,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1865410",
      "title": "Applying EASTL ethical constructs in AI-driven CRM: the mediating role of sustainable customer trust in enhancing customer retention in private sector banks",
      "summary": "The rapid integration of Artificial Intelligence (AI) in banking, Customer Relationship Management (CRM) has undergone several changes, which have also led to ethical concerns and implications on customer outcomes. While previous studies have studied AI adoption and trust independently, little attention has been placed on how ethical AI dimensions interact and affect customer retention via trust mechanisms. In this sense the objective of our study is to investigate ethical AI practices (using th",
      "authors": "A. Geetha",
      "category": "research",
      "topics": "finance-investment",
      "published_at": "2026-08-03T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/15712"
    },
    {
      "id": 15935,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1825189",
      "title": "Utilization of Artificial Intelligence to support administrative decision-making in special education institutions in Saudi Arabia: perceptions of principals, supervisors, and teachers",
      "summary": "Artificial Intelligence (AI) is increasingly used in educational administration to automate routine tasks, support data-informed decision-making, and improve institutional efficiency. This study examined the perceptions of principals, supervisors, and teachers regarding the utilization of AI to support administrative decision-making in special education institutions and investigated whether these perceptions differed according to professional role, years of experience, and AI-related training. T",
      "authors": "Abdulaziz Alsuhaymi",
      "category": "research",
      "topics": "children-education,finance-investment",
      "published_at": "2026-08-03T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/15935"
    },
    {
      "id": 15936,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1833043",
      "title": "Ethical use of artificial intelligence in education: proposed ethical competency framework for teachers",
      "summary": "IntroductionThe rapid integration of Artificial Intelligence (AI) in education is transforming teaching, learning, and professional development through personalized instruction, adaptive learning, and data-driven educational practices. However, in Pakistan and Saudi Arabia, many teachers still lack the ethical awareness and practical competencies required for the responsible use of AI technologies. Challenges related to data privacy, algorithmic bias, transparency, accountability, and equitable",
      "authors": "Ibrahim Yaussef Alyoussef",
      "category": "research",
      "topics": "bias-fairness,privacy-surveillance,children-education,transparency",
      "published_at": "2026-08-03T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/15936"
    },
    {
      "id": 15404,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1752349",
      "title": "Trust by design in AI-augmented procurement systems: the roles of explainability, governance, and human oversight",
      "summary": "With the growing integration of artificial intelligence (AI) into buyer–supplier negotiations, procurement teams must translate efficiency gains into defensible and appropriately calibrated reliance on AI-mediated decision support. This study develops and empirically evaluates a socio-technical trust-by-design model for AI-augmented procurement negotiation systems. It jointly considers perceived transparency/explainability (XAI), ethical governance visibility, and human-in-the-loop (HIL) relatio",
      "authors": "Raja Mejri",
      "category": "research",
      "topics": "regulation,transparency",
      "published_at": "2026-07-31T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/15404"
    },
    {
      "id": 15002,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1878977",
      "title": "A comparative analysis of pretrained Wav2Vec XLSR-53 and Whisper-Small models for automatic speech recognition in the Telugu language",
      "summary": "This study presents the development of an automatic speech recognition (ASR) system tailored for Telugu, one of the widely spoken Indian languages. In recent years, deep learning (DL) techniques have been applied to develop ASR systems across various languages and domains. These models, however, require substantial training resources and extensive corpora of continuous speech composed from multiple dialectal speakers, along with their corresponding transcripts. This paper investigates the effect",
      "authors": "Jagalingam Pushparaj",
      "category": "research",
      "topics": "finance-investment",
      "published_at": "2026-07-30T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/15002"
    },
    {
      "id": 15003,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1890320",
      "title": "Artificial intelligence for early prediction of gestational diabetes mellitus and preeclampsia: a systematic review of machine learning models and clinical decision support systems",
      "summary": "Gestational diabetes mellitus (GDM) and preeclampsia are among the most significant pregnancy complications, affecting approximately 5–15% and 2–8% of pregnancies worldwide, respectively. These disorders share overlapping metabolic, vascular, inflammatory, and placental mechanisms, highlighting the need for integrated approaches to early prediction and risk assessment. However, existing artificial intelligence (AI)-based prediction models generally address GDM and preeclampsia independently and",
      "authors": "Subhashree Barada",
      "category": "research",
      "topics": "healthcare",
      "published_at": "2026-07-30T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/15003"
    },
    {
      "id": 14226,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1768178",
      "title": "Information bubble and skill evolution: a theoretical framework for integrating distributed cognition, cognition offloading, and metacognitive regulation based on AIOE",
      "summary": null,
      "authors": "Tansheng Lu",
      "category": "research",
      "topics": "regulation",
      "published_at": "2026-07-29T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/14226"
    },
    {
      "id": 14639,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1874825",
      "title": "DrugPred: an EdgeConv-GNN and Bio_ClinicalBERT based polypharmacy ADR prediction and specialist recommendation model",
      "summary": "Adverse drug reactions (ADRs) are caused by medication and are considered a serious issue in healthcare when there is simultaneous use of different medications resulting in drug-drug interaction (DDI). Traditional approaches mostly focus on the effects caused by a single drug, and they fail to capture the side effects from drug combinations. In this research work, a deep learning-based DrugPred framework is proposed to predict ADR risks by integrating individual drug effects, interaction statist",
      "authors": "J. G. Arjay",
      "category": "research",
      "topics": "healthcare",
      "published_at": "2026-07-29T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/14639"
    },
    {
      "id": 14640,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1808028",
      "title": "Bridging agronomic science and context specific farm-level advisory through generative AI for rice systems in India",
      "summary": "Agriculture is increasingly characterized by a data paradox, while the sector generates massive volumes of genomic, climatic, remote sensing, and field data. Translating this information into actionable, farm-level insights remains a critical bottleneck. Traditional advisory mechanisms cannot operate at the spatial scales or provide the context-specificity needed for climate adaptation and food security. The work presents GenAI as a transformative interface that makes advanced agricultural scien",
      "authors": "Shalini Gakhar",
      "category": "research",
      "topics": "environment,biotech",
      "published_at": "2026-07-29T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/14640"
    },
    {
      "id": 14641,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1870819",
      "title": "Continuous assurance for AI-driven clinical decision support systems",
      "summary": "Healthcare systems are rapidly embedding adaptive and generative AI into core clinical processes. The integration of Artificial Intelligence into Clinical Decision Support Systems (AI-CDSS) highlights a fundamental transformation within healthcare delivery. This transformation enables advanced predictive analytics, multimodal data integration, and real-time augmentation of clinical decisions. However, AI introduces systemic, ethical, operational, and governance risks that challenge traditional h",
      "authors": "Rami A. Al-Horani",
      "category": "research",
      "topics": "regulation,healthcare",
      "published_at": "2026-07-29T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/14641"
    },
    {
      "id": 14642,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1837513",
      "title": "A comparative evaluation of quantum machine learning architectures for breast cancer classification using clinical and genomic data",
      "summary": "IntroductionIn recent years, high-dimensional clinical and genomic data have gained significant importance for prognosis and personalized medicine in breast cancer. But the use of quantum machine learning (QML) on such data is limited by the availability of few qubits, the computation time of quantum simulation, and dimensionality reduction. This work systematically compares several QML architectures for breast cancer classification in the presence of realistic and simulator constraints.MethodsT",
      "authors": "Saartak Allena",
      "category": "research",
      "topics": "healthcare,biotech",
      "published_at": "2026-07-29T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/14642"
    },
    {
      "id": 14643,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1861374",
      "title": "A hierarchical federated learning framework with FedNova, game-theoretic matching, and QKD-assisted privacy for the internet of vehicles",
      "summary": "The Internet of Vehicles (IoV) supports essential intelligent transportation applications but encounters challenges in federated learning (FL) due to non-independent and identically distributed (non-IID) data, vehicle mobility, resource heterogeneity, and strict privacy requirements in latency-sensitive scenarios such as misbehavior detection and accident response. Traditional FL methods, such as random client selection and standard FedAvg, often experience slow convergence and reduced performan",
      "authors": "L. Jai Vinita",
      "category": "research",
      "topics": "privacy-surveillance",
      "published_at": "2026-07-29T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/14643"
    },
    {
      "id": 14644,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1785085",
      "title": "“But it sounded confident”: the role of accuracy, tone, and disclaimers in users' medical decision-making",
      "summary": "IntroductionArtificial intelligence (AI)-powered chatbots are increasingly used in healthcare for applications ranging from symptom triage to lifestyle guidance. Their effectiveness depends not only on their ability to provide reliable information but also on users engaging with their advice while remaining aware of potential inaccuracies. This study investigated how users perceive AI-generated medical advice, with a particular focus on the roles of accuracy, conversational tone, and disclaimers",
      "authors": "Kerstin Denecke",
      "category": "research",
      "topics": "healthcare,finance-investment",
      "published_at": "2026-07-29T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/14644"
    },
    {
      "id": 14645,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1785766",
      "title": "Overview of RAG-based and LLM-based approaches to personalization in healthcare AI applications",
      "summary": "Recent advances in Large Language Models (LLMs), driven by transformer architectures such as Generative Pre-Trained Transformer (GPT), are opening new frontiers in healthcare Artificial Intelligence (AI) by enabling clinically relevant interactions between patients and clinicians. Yet persistent challenges—including limited real-time knowledge access, safety concerns and insufficient patient-centered contextualization—indicate that current systems often fall short in delivering efficient and rel",
      "authors": "Manal Althobaiti",
      "category": "research",
      "topics": "healthcare",
      "published_at": "2026-07-29T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/14645"
    },
    {
      "id": 14227,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1881187",
      "title": "PredictRx: AI based decision support tool for molecular screening for breast cancer drug recommendation",
      "summary": "IntroductionBreast cancer remains one of the leading causes of cancer-related mortality rate worldwide, and the identification of effective drug combinations is an essential requirement in pharmaceutical research. The integration of Artificial Intelligence (AI) in processing large volumes of chemical and biological data combines molecular representation, predictive modeling and structured support within a single accessible tool, which accelerates early-stage candidate identification for breast c",
      "authors": "Ritu Chauhan",
      "category": "research",
      "topics": "healthcare,biotech",
      "published_at": "2026-07-28T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/14227"
    },
    {
      "id": 14228,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1813190",
      "title": "CNN-RNN framework for lung cancer classification using CT imaging and GAN-based augmentation",
      "summary": "Lung cancer remains one of the leading causes of cancer-related deaths worldwide, and early identification of malignant abnormalities plays an important role in improving patient survival rates. However, accurate lung cancer classification using CT imaging remains challenging because of limited dataset availability, class imbalance, overlapping lesion characteristics, and lack of interpretability in existing deep learning systems. This study presents a GenAI-driven CNN–RNN framework for explaina",
      "authors": "Bodicherla Siva Sankar",
      "category": "research",
      "topics": "safety-alignment,healthcare",
      "published_at": "2026-07-28T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/14228"
    },
    {
      "id": 14229,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1861135",
      "title": "Radiomics-driven and explainable machine learning for rapid characterization of Fusarium wilt and Black Sigatoka in banana crops",
      "summary": "IntroductionBanana production is increasingly threatened by fungal diseases such as Fusarium wilt and Black Sigatoka, posing severe risks to food security and agricultural economies. Recent image-based approaches using deep learning have shown high predictive capacity for plant disease recognition; however, their limited transparency, calibration uncertainty, and sensitivity to domain shifts can restrict their use in decision-support workflows that require auditability.MethodsThis study proposes",
      "authors": "Rodne Andrés Quijije",
      "category": "research",
      "topics": "transparency",
      "published_at": "2026-07-28T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/14229"
    },
    {
      "id": 13870,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1849315",
      "title": "TriFusion-ADFormer: a deep learning framework for early Alzheimer’s disease detection using MRI and cognitive metrics",
      "summary": "IntroductionAlzheimer’s disease (AD) is a progressive neurodegenerative disorder with the gradual loss of cognitive functions and neuronal degeneration. Early and accurate diagnosis is essential for timely therapeutic intervention and improved patient management. However, effectively integrating complementary multimodal information for reliable AD classification remains a significant challenge.MethodsThis study proposes TriFusion-ADFormer, a multimodal deep learning framework for multiclass clas",
      "authors": "S. Sabari Vasan",
      "category": "research",
      "topics": "healthcare",
      "published_at": "2026-07-27T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/13870"
    },
    {
      "id": 13871,
      "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1885910",
      "title": "TRuE-XAI: causal and explainable ai framework for trustworthy corporate earnings growth forecasting",
      "summary": "Forecasting corporate earnings growth is fundamental to investment, credit, and regulatory decision-making. Existing forecasting approaches either rely on restrictive linear assumptions or provide limited interpretability, making them less suitable for high-stakes financial applications. This study proposes a transparent and causally informed framework for predicting future corporate earnings growth from financial statement data. We present TRuE-XAI (Transparent, Rule-based, and Explainable Arti",
      "authors": "Gopal Singh Jamnal",
      "category": "research",
      "topics": "regulation,safety-alignment,transparency,finance-investment",
      "published_at": "2026-07-27T00:00:00.000Z",
      "source": "Frontiers in Artificial Intelligence",
      "ethics_ai_record_url": "https://ethics.ai/record/13871"
    }
  ],
  "attribution": "via ethics.ai"
}