explainable ai

Heterogeneous bus ridership responses to micro-scale built environment: Evidence from explainable AI

Meaningful human authority in AI mediated decision making an authority vector and bottleneck principle

Is AI a moral expert?

It is concluded that, while AI may be considered a valuable tool for supporting human moral deliberation, it cannot by itself serve as an expert moral decision-maker.

A comprehensive review of AI-based approaches for lung disease classification using chest X-ray images

Artificial Intelligence for Drug-Likeness Prediction: An Intelligent Framework for Chemical Compound Screening

An AI-based framework for predicting drug-likeness using molecular descriptors derived from chemical structures is proposed that integrates data preprocessing, feature selection, machine learning model development, and performance evaluation using metrics such as accuracy, precision, recall, F1-score, and AUROC.

The Role of AI in Enhancing Financial Decision-Making in Commerce

It is argued that AI improves financial decisions not by replacing judgement but by reallocating it, and it outlines implications for enterprises, regulators and educators, with particular attention to small businesses in emerging economies.

Quantum sovereignty and constitutional asymmetry: mapping the sovereignty gap in Puerto Rico and the Dominican Republic

Quantum technologies are advancing faster than the institutional capacities of most small island jurisdictions, creating a structural vulnerability that remains largely unaddressed. This article introduces the Quantum Sovereignty Gap (QSG) to explain why Puerto Rico (PR) and the Dominican Republic (DR) risk entering th...

An XGBoost–logistic regression hybrid stacking model for breast cancer outcome prediction

An AICc-guided hybrid stacking framework that integrates Logistic Regression and XGBoost using the SEER breast cancer dataset achieves a balanced combination of predictive performance, calibration, robustness, interpretability, and model parsimony, providing reliable and clinically meaningful support for breast cancer...

A context-aware and human-centered framework for road safety assessment using semantic scene understanding

The proposed framework provides a reliable and explainable approach for assessing perceived road safety by integrating semantic scene understanding with contextual reasoning and establishes a reproducible foundation for future research in interpretable transportation systems, human–AI alignment analysis, and road scene...

AI Driven Multidomain Research: New Frontiers in Science, Business and Technology

The Multidomain AI Research Integration framework is proposed, which explains cross-domain research value through three interacting layers: a foundation layer of shared data, compute and models; a translation layer comprising four transfer mechanisms (method, data and representation, problem-structure, and talent and t...

Explainable Dual-view Workflow Discovery for GenAI-supported Learning in Higher Education

Mitigating human review bottlenecks in AI evaluation pipelines: A queueing theory approach

Human review stages in AI-assisted evaluation pipelines can become throughput bottlenecks when automated upstream processes generate cases faster than reviewers can assess them—a problem acute when information availability varies across client types, a form of structural information asymmetry whose operational conseque...