explainable ai

Unravelling the role of increased model resolution in simulating surface temperature fields using explainable AI

A convolutional neural network classifier combined with explainable AI is employed to assess the role of resolution in simulating winter surface temperature fields in an ensemble of 17 control climate simulations with varying oceanic and atmospheric resolutions, highlighting the central role of ocean resolution in shap...

Impact of Artificial Intelligence-Supported Education on Academic Engagement, Psychological Well-Being, and Career Development among University Students

Overall, the findings suggest that AI should be integrated as a supportive educational resource rather than a replacement for teachers, counselors, or students’ independent decision-making.

PERSPECTIVES FROM SELECT INDIAN IT COMPANIES ON AI GOVERNANCE: EVIDENCE FROM A SURVEY AND EXPERT INTERVIEWS

The findings suggest that Responsible AI Governance in Indian IT companies is not built by policy statements alone and grows where regulatory readiness and ethical culture are taken seriously and where governance is treated as an organisational capability rather than a compliance exercise.

Relational Drivers and Technical Efficiency in Indonesian Banana Value Chains: A Multi-Actor Regression-DEA Analysis

Background: Indonesia is the world’s third-largest banana producer, yet its value chains remain structurally fragmented, with pervasive information asymmetries and substantial technical inefficiencies among smallholder farmers. Research integrating relational governance determinants with frontier efficiency analysis ac...

Explainable AI molecularly imprinted polymer electrochemiluminescence biosensor for discriminative methamphetamine and amphetamine detection

Summary Independent forensic quantification of methamphetamine (MA) and amphetamine (AMP) remains challenging due to their near-identical structures and overlapping electrochemical signatures. We report an end-to-end AI-integrated MIP-ECL biosensing framework employing target-specific MIP-NP/MWCNT/Nafion/Ir(pq)2(acac)/...

The context of AI transformation: perceptions of AI ethicality and their impact on trust and use intentions

Introduction As the implementation of AI accelerates within organizations, concerns emerge about the ethicality of AI-driven systems and tools and the ways people use them. Although previous literature proposes numerous theories regarding how to develop and use AI ethically, questions remain about how users perceive th...

An Explanatory Study of University Students’ Satisfaction with AI-Based Counseling Agents

Preoperative differentiation of spinal tuberculosis, pyogenic, and brucellar spondylitis: a multimodal machine learning study across five centers

Background Infectious spondylitis, encompassing spinal tuberculosis (STB), pyogenic spondylitis (PS), and brucellar spondylitis (BS), is difficult to diagnose preoperatively because the three entities present with overlapping clinical features. Differentiation is often delayed by prolonged culture turnaround times and...

The framework of Large Mathematical Logic Model (LMLM): Gibbs sampling-based restricted Boltzmann machines for financial engineering

This paper gives a mathematical account of Gibbs sampling, the classical Boltzmann Machine (BM), and the efficiency breakthrough of the Restricted Boltzmann Machine (RBM), tracing the Markov Chain Monte Carlo lineage from Metropolis–Hastings to coordinate-wise sampling and deriving the Contrastive Divergence (CD k ) al...

Cognitive Performance Under AI Advice: Development and Initial Validation of a CHC-Informed Assessment for Organizational Decision-Making

Artificial intelligence (AI) is increasingly embedded in organizational decision-making, requiring employees not only to use AI-generated recommendations but also to evaluate their quality and determine when reliance is appropriate. Although established research examines behavioral reliance on algorithmic and AI advice...

AI Explanations Through the IT Artifact Views

Explainable Fraud Detection AI System in Financial Sector

The study shows that ensemble models on the original feature space provide highly accurate and stable fraud detection on this dataset and SHAP analysis reveals that source and destination balances, transaction amount and type are the most influential features.