explainable ai

AI-Assisted Risk and Incident Analysis: Advances and Future Opportunities

Operational risk and security incidents now unfold across distributed, cloud-native estates that generate volumes of telemetry no human team can manually triage at speed. Over the past decade, artificial intelligence (AI) and machine learning (ML) have moved from experimental log-mining prototypes to production compone...

A Depth Psychology Stance on Consciousness and the Unconscious

This chapter demonstrates how the foundational depth psychology theorists—C.G. Jung, Marie-Lousie von Franz, James Hillman, and Marion Woodman—have shared work concerning the modern West&s;s struggle from pressures on consciousness. The four theorists’ s work in this chapter introduces key concepts for evolving conscio...

A drift-aware explainable artificial intelligence framework for dynamic construction risk governance using multi-domain fuzzy inference and Bayesian updating

Purpose Construction projects operate in highly dynamic environments where project risks evolve continuously as new information becomes available. Conventional risk assessment approaches are predominantly static and provide limited capability to adapt to temporal changes or generate transparent decision support. This s...

The Application of AI-Based Technology and Psychological Contract in Improving Employee Performance in the Hotel Industry

The findings extend hospitality research by integrating technological and psychological perspectives within a single model and indicate that AI utilization and PC fulfillment are associated with employee performance both directly and through innovative work behavior.

Deterministic and stochastic interventions in reducing drug–drug interactions in inappropriate prescribing: A systematic review

Current evidence does not demonstrate a proportional improvement in clinically reliable decision support in stochastic and generative models for DDI detection, and future interventions must prioritize hybrid architectures that integrate explainable rule-based guardrails with rigorously validated stochastic models.

Collaborating With Artificial Intelligence in Higher Education

This chapter presents a teaching and assessment approach that incorporates generative artificial intelligence (GenAI) into learning while maintaining a focus on students' developing understanding. Drawing on a portfolio-and-interview model implemented in Mathematical Statistics, the approach combines longitudinal portf...

Situated AI Literacy Change Through GenAI-Supported Simulation in Secondary Physics: Field Evidence from Curriculum-Integrated Inquiry

Overcoming Optical Blindness in Tropical Earth Systems: A Cloud-Based Sentinel-1 Big Data and Explainable AI Framework for Flood Susceptibility Mapping

A cloud-based, explainable framework that integrates big-data processing of Sentinel-1 Synthetic Aperture Radar on Google Earth Engine with tree-based bagging and boosting ensembles in the Takkalasi watershed, Indonesia provides a reproducible and transferable basis for ecosystem-based flood adaptation in data-scarce t...

Too Good to Be Real? Diagnosing and Reducing the Gap Between AI Preference and Real User Engagement

This work proposes Ontology-Masked Reasoning Autoencoding (OMRA), a controlled intervention that masks and reconstructs over-explained spans while preserving stance, factual content, and coherence and reduces the measured gap between AI preference and real user engagement.

Applications and challenges of artificial intelligence in cancer management

Artificial intelligence (AI) is increasingly transforming cancer management by enabling the analysis of complex multimodal data generated across the cancer care continuum. This structured narrative review synthesizes current applications of AI, machine learning, and deep learning in cancer detection, imaging diagnosis,...

Interaction-induced knowledge narrowing risk in LLM systems

AI governance frameworks address well-documented risks through controls on data, models, and post-deployment performance. This paper identifies a distinct, under-governed lifecycle risk arising not from model error or degradation, but from interaction itself. Termed Interaction-Induced Knowledge Narrowing (IIKN), this...

Smooth Muscle Cell Plasticity as a Central Determinant of Plaque Stability

This review examines how smooth muscle cell (SMC) fate transitions contribute to the central paradox of atherosclerosis: why thin-cap plaques that only mildly narrow the lumen cause most acute events, whereas many severely stenotic lesions remain stable. We propose that interactions between SMC developmental programmin...