explainable ai

How Artificial Intelligence Facilitates Collaborative Governance? Research on Mechanisms and Multi‐Dimensional Effects Based on Triple Entities

A quantifiable multi‐stakeholder governance framework and actionable policy insights for China and the Global South are offered and econometric evidence from difference‐in‐differences, threshold regressions, and spatial econometric models robustly addresses concerns about AI.

ARTIFICIAL INTELLIGENCE-ENABLED REGTECH FOR KYC, FINANCIAL COMPLIANCE, AND REGULATORY REPORTING: APPLICATIONS, RISKS, AND FUTURE DIRECTIONS

This narrative review explains the technical architecture of artificial intelligence-enabled RegTech systems and examines their use in know-your-customer procedures, financial compliance monitoring, investigations, and regulatory reporting.

Is AI Becoming the New Manager?

This blog examines how AI-based advice seeking may affect managerial legitimacy and manager-employee relationships and argues that the important issue is not whether AI will formally replace managers, but whether increasing dependence on AI for workplace advice will change how employees perceive and use human managers.

Artificial intelligence capability and project performance: integrating dynamic capabilities theory and the knowledge-based view

The findings indicate that AI capability positively influences knowledge integration, project agility, and project performance, and that AI capability improves project performance by strengthening knowledge integration, which subsequently enhances project agility.

Explainable AI for evaluating hospitalization outcomes based on electronic health records for chronic disease patients

Learning human aligned explanations through supervised aggregation of XAI methods for Arabic sentiment analysis

This study provides the first investigation of human-supervised XAI aggregation in Arabic, offering new insights into balancing faithfulness and plausibility in explainable NLP.

Structural links between AI-art literacy, VR presence, visual-language proficiency, and experimental imaging performance among university students.

Novelty lies in a production-centered, theory-consistent pathway from domain-specific AI-art literacy to an authentic, rubric-and-logs outcome via visual-language proficiency, with VR presence as a boundary condition, delivered through a reflective-formative PLS-SEM architecture and a large, multi-city Chinese sample.

The Patient Beyond the Dataset: Levinas, Magnifica Humanitas, and the Principle of Non-totalization in AI Healthcare in the Philippines.

The article argues that AI may inform clinical judgment without acquiring morally exhaustive authority over the person to whom healthcare remains answerable and concludes that AI may inform clinical judgment without acquiring morally exhaustive authority over the person to whom healthcare remains answerable.

Translational Insights From the Molecular Landscape to Precision Therapy in Peripheral T-Cell Lymphoma: Integrating Multi-Omics and Artificial Intelligence.

This review synthesizes current molecular insights, evaluates the limitations of existing therapies, and explores the potential of multi-omics profiling integrated with artificial intelligence (AI) to decode complex signatures for improved subtype classification, treatment response prediction, and survival prognosticat...

Validation of the generative artificial intelligence dependency scale among Chinese college students.

The Chinese version of the Generative Artificial Intelligence (AI) Dependency Scale is a useful assessment tool for determining generative AI dependency among Chinese university students because it shows strong validity, reliability, and measurement invariance.

ScopeMamba-YOLO: Widening the Perceptual Scope Inward and Outward for Small Object Detection in Remote Sensing Imagery

This work proposes ScopeMamba-YOLO, built around an off-path, zero-gated selective-scanning principle that decouples contextual modeling from the convolutional stream, and shows consistent improvements on AI-TOD, especially for very-tiny and tiny objects.

AutoScreen: AI Co-Scientist System for Target Discovery in Functional Genomics

This work introduces AutoScreen, an AI co-scientist system supporting target discovery through both Pre-screen Design, which constructs perturbation libraries de novo from free-text research descriptions, and Post-screen Analysis, which re-ranks experimental screen hits by integrating statistical scores with biological...