explainable ai

Machine Learning in E-Commerce: Trends, Applications, and Future Challenges

E-commerce platforms continuously generate large amounts of data from customer searches, browsing activities, purchases, reviews, and delivery operations. Machine learning (ML) turns this data into useful predictions and decisions that improve product recommendations, pricing, fraud detection, inventory management, and...

Real-Time Dynamic Pricing and Revenue Management in Hospitality 5.0

This chapter discusses upgrades in pricing strategies for the hospitality industry. It will analyze real-time dynamic pricing and advanced revenue management systems with reference to the framework of Hospitality 5.0. AI, IoT, and data analytics help to advance pricing strategies from basic static models to cutting-edg...

Measuring University Students' Ethical Judgments of Generative AI Use: Development and Initial Validation of Four Instruments for Academic Integrity Research

Designing responsible AI feedback in higher education: faculty readiness, student judgement, and metacognitive calibration

Artificial intelligence (AI) is now part of everyday academic work in higher education, including feedback. However, AI feedback does not automatically lead to learning. It can help students rethink a concept, improve a draft, and notice gaps in their work, but it can also create a false sense of progress when students...

From Human Judgment to Machine Learning: A Philosophical Study of Knowledge Creation in Modern Accounting

Background: The integration of artificial intelligence (AI) and machine learning into accounting and auditing has transformed the creation, validation, and application of professional knowledge, raising questions about the legitimacy of algorithmic knowledge in accounting practice. Objective: This study examines the ep...

Diagnostic Performance of Deep Learning Models for Ocular Toxoplasmosis Using Fundus Photography: A Systematic Review and Meta-Analysis.

BACKGROUND Ocular toxoplasmosis (OT) is the leading infectious cause of posterior uveitis worldwide, yet diagnosis remains challenging without a universal gold standard. Although deep learning (DL) shows significant promise for automated image analysis, a rigorous synthesis of its diagnostic accuracy across heterogeneo...

Who Pays the Review Cost? Triage, Fairness, and Accountability in AI-authored Pull Requests

AIPR governance is reframe around making human judgment observable before generated contributions consume scarce reviewer attention, and conceptualize missing rationale, validation, and ownership as reviewability debt, the work reviewers must absorb when generated code lacks sufficient human grounding.

The Polytopal Neural Network

Understanding how deep neural networks process information remains a central challenge. Existing interpretability methods often compromise structural fidelity, rely on prespecified corpora, or explain models post-hoc. We propose Polytopal Neural Networks (PNNs), a framework that extracts distinct layer-wise aspects by...

Writing for the Reviewer: Defensive Writing in GPT Models

Researchers increasingly use ChatGPT to revise their papers, and recent GPT versions often narrow or even retract the authors'claims. We call such changes defensive writing when the given material does not support them, and we test two explanations: the model corrects the authors'overclaiming, or it writes for an antic...

Explainable AI for Silent Depression and Mental Fatigue Detection: A Comprehensive Review of Facial Micro-Expressions, Eye Dynamics, and Multimodal Behavioral Analysis

Due to their impact on emotional health, mental functioning, productivity and quality of life, depression and mental fatigue have become public health problems.Early diagnosis and early intervention are important to the successful treatment of these conditions.Thanks to the recent developments in Artificial Intelligenc...

From search results to generative answers: A mixed-methods study of credibility assessment and trust formation in AI Overviews

Generative search shifts information evaluation from comparing ranked documents to assessing synthesised answers. This study examines how users’ perceptions of answer quality are associated with trust and credibility in Google AI Overviews and explores whether visible interface cues may foster trust without systematic...

Physics-Informed and Explainable Machine Learning for Expansive Soil Swell–Shrink Dynamics: A Critical Review of Computational Architectures, Constraints, and Validation Paradigms in Geotechnical AI

Expansive soil prediction involves more than estimating a swelling index from classification properties. Engineering decisions depend on path-dependent deformation, evolving water retention, restraint, fabric change and uncertain environmental forcing. Physics-informed and explainable machine learning offer complementa...