explainable ai

Actualizing artificial intelligence (AI) affordances in urban mobility organizations: a hierarchically interconnected perspective

How AI value in urban mobility emerges through conditional enablement across infrastructure, operations, services and sustainability layers, and contributes to enterprise information management by explaining how data, systems, governance and ecosystem coordination shape interconnected AI-enabled value creation is shown...

Delegating decision authority to AI in recruitment: an empirical study of trust, control and algorithmic accountability

The widespread adoption of artificial intelligence (AI) in human resource (HR) practices has led to a growing number of organizations trying to understand the use of AI in recruitment. While AI is increasingly embedded in HR practices, few studies focus on how trust in AI is formed and how it shapes the adoption of...

Artificial intelligence in human microbiome research: from data integration to explainable translation

This narrative review synthesizes literature from 2015 to 2025 without a formal systematic methodology and concludes with three priority areas for AI-microbiome translation: improved interpretability, longitudinal modeling, and clinical integration.

Multimodal feature importance ranking improves classroom teaching quality evaluation in human and artificial intelligence collaborative education

Classroom Teaching Quality Evaluation (CTQE) constitutes a core component of human-AI collaborative education systems; however, existing methods exhibit significant deficiencies in the systematic importance ranking of multimodal features. This paper proposes the Multimodal Feature Importance Ranking via Multi-model Ens...

EFFECT OF AI-POWERED SKILLS ON LEARNING OUTCOMES OF ND/HND ACCOUNTANCY STUDENTS IN FINANCIAL ACCOUNTING IN WAZIRI UMARU FEDERAL POLYTECHNIC, BIRNIN KEBBI

This study examined the effect of AI-powered skills on learning outcomes of ND/HND Accountancy students in Financial Accounting in Waziri Umaru Federal Polytechnic, Birnin Kebbi, Nigeria. The study adopted quasi-experimental research design of pre-test post-test non-equivalent control group. Two research questions and...

An Adaptive Weighted Interpretable Multi-Scale CNN Model for Multi-Class Skin Lesion Classification

Health issues are among the major concerns for people nowadays. Cancer remains one of the leading causes of mortality worldwide, and many are suffering from it. Out of these, skin cancer is also one of its kind and the deadliest. According to estimates, the number of new melanoma cases has surged in recent years, and t...

Product Architecture and Unified Consumer Pricing

Digital markets display a common monetization contrast: broad common interfaces often rely substantially on advertisers, merchants, enterprise customers, or other business-side actors, whereas narrower products more readily sustain direct consumer pricing. Existing research explains important parts of this contrast thr...

Explainable Artificial Intelligence for Early Detection of Chronic Diseases in Saudi Healthcare: A Systematic Review

Background: Explainable artificial intelligence (XAI) may support earlier detection of chronic disease, but Saudi-specific evidence has not been systematically characterized. Objective: To synthesize Saudi patient-level studies of AI/XAI for chronic-disease prediction and assess clinical readiness. Methods: PubMed and...

AI‑BASED VOICE ASSISTANTS IN FAMILIES WITH PRESCHOOL‑AGE CHILDREN: RISKS OF A DEFICIT IN THE EMOTIONAL TRANSMISSION OF FAIRY‑TALE NARRATIVE

This article is devoted to studying the development of emotional intelligence in preschool‑age children when conveying a fairy‑tale narrative using an AI‑based voice assistant. The scientific novelty of the study lies in examining the interaction between preschool‑age children and voice assistants and identifying the s...

Development and Evaluation of Enhanced Machine Learning Models for Tumor Severity Classification Through Integrated Feature Extraction and Selection Techniques

The precise categorisation of the severity of tumours is an important function in medicine, as it is a vital part of deciding treatment and predicting patient outcome. We offer an integrated pipeline for developing and evaluating the latest machine learning methods for tumour severity classification. We apply advanced...

A Systematic Literature Review of Machine Learning, Deep Learning, and Explainable AI for Predicting Mechanical Performance and Microplastic Release in Plastic Waste-Modified Asphalt

The growing pile of plastic waste has led to the implementation of sustainable engineering solutions, such as putting recycled plastics into asphalt mixtures, referred to as Plastic Waste-Modified Asphalt (PWMA). Despite the fact that in earlier studies, a significant improvement in mechanical performance, particularly...

Understanding preferences for explainable AI: A study on LIME variations.