explainable ai

Artificial intelligence anxiety and academic burnout among Chinese postgraduate students: perceived stress and rumination as serial mediators

Introduction This study examined whether perceived stress and rumination help explain why postgraduate students with higher artificial intelligence (AI) anxiety also report greater academic burnout. Methods We analysed data from a cross-sectional online questionnaire survey conducted among postgraduate students in Guan...

Scale-free Niche Construction: expanding agent-microenvironment co-development to unconventional substrates

Niche construction emphasizes the active aspect of life forms that alter their environment via feedback loops in which that environment inevitably changes their behavior, structure, and future evolution. We argue that this powerful dynamic is general, extending far beyond typical applications in ecology and evolutionar...

AI Quality and Clinical Roles Impact Decision Quality in AI-Augmented Medical Decisions More than AI Explanations.

The findings suggest that detailed AI explanations do not necessarily improve AI-augmented medical decisions even though they can increase trust in AI recommendations and the importance of AI recommendation quality in clinical decision making is underscored.

Adoption of AI for Travel: Corporate Strategies and Firm Value

Tourists’ responses to the adoption and implementation of AI-based travel solutions have received considerable scholarly attention, yet investors’ reactions to such adoption decisions remain insufficiently understood. Drawing on multiple intelligences and tourism innovation archetypes theories, this study develops a co...

A Threat Modeling Prioritization and Automation Framework for Composable Architectures

A snapshot of the literature review of the threat modeling for composable architectures is offered, why automation is difficult in this context is shown, and an automation framework to allocate scarce resources according to risk exposure to composable architecture components is proposed.

AgentPort-Bench: A Controlled Seven-Framework Evaluation of Agentic AI Security Portability

Decentralized blockchain governance for explainable causal AI in macroeconomic forecasting

The increasing occurrence of structural breaks, financial crises, and economic uncertainty has significantly weakened the predictive performance of traditional macroeconomic forecasting models. In response to these limitations, this study proposes a blockchain-enabled causal machine learning framework designed to impro...

A mutually reinforcing pixel-level deep network-driven explainable AI for biofouling segmentation and structural health monitoring in marine environments

Marine biofouling is a disadvantage to submerged structures because it reduces the hydrodynamic efficiency, the structural integrity, and the operational costs, hence the necessity of conducting accurate inspection and monitoring as part of marine infrastructure management.. Although underwater imaging systems enable l...

Explaining Cross-National Variations in AI Policy Enactment: Indonesia, Finland, and South Korea

The development of artificial intelligence in education has outpaced the institutionalisation of policies governing its use, accountability, and implementation. Cross-national scholarship remains dominated by discussions of adoption, pedagogical opportunities, and ethical risks, while variation in policy enactment acro...

Responsible AI Governance for Vaccine Safety and Pharmacovigilance in Nigeria: A Policy Review of Risks, Regulatory Gaps, and Implementation Pathways

Modality and cultural tailoring in human–AI disaster communication: examining mechanisms and outcomes for Hispanic communities

Purpose This study examines how GenAI chatbots can support Hurricane preparedness among Hispanic/Latino communities by comparing combined audio-text delivery with text-only delivery and culturally tailored with generic content. It focuses on information overload and emotional attachment as cognitive and affective mecha...

The impact of deep learning and omics data in transforming precision therapy for brain cancer

The combination of AI with high-throughput genomics has revolutionized oncology, particularly in the case of brain cancer, a diagnostically complicated and heterogeneous tumor. With the explosion of omics data and computational power, deep learning (DL) has evolved as a powerful computational approach for the interpret...