explainable ai

Pathway to achieve sustainable value through task-AI fit: the critical roles of green knowledge management capabilities, competitive advantage and financial resilience

This study aims to investigate how artificial intelligence (AI)-based technologies are associated with sustainable value intentions in green-oriented organisations operating under circular economy principles in China. Drawing on an adapted view of task-technology fit and the resource-based view (RBV), this study ex...

Using Artificial Intelligence to Achieve Inclusive and Effective Education

Artificial intelligence, as one of the transformative technologies of the contemporary era, has considerable capacity to respond to learners’ individual differences, reduce barriers to access to education, and improve the quality of the teaching–learning process. The present study was conducted with the aim of explaini...

Are Feature Selection and Feature Attribution the Same? A Comparative Survey -- Extended Version

Dynamic and Explainable Deep Learning Models

This paper presents a novel approach to developing dynamic and explainable deep learning models. The core challenge in deploying deep learning systems is often their "black box" nature, hindering trust and adoption. This work addresses this issue by integrating Explainable Artificial Intelligence (XAI) techniques with...

Intuitions and Directional Shifts in Minimal Computation Cosmology

This research genealogy traces the development of Minimal Computation Cosmology from its earliest prime-based intuitions to the separation of LAW and SOURCE, the Wonsik Reality-Renderer Architecture (WRRA), and a non-branching universe with a single accumulated history. Rather than presenting the model as a completed p...

AI-Based Phishing Detection: A Review and Practical Study

This paper presents a review and practical study of AI-based phishing detection. It explains how machine learning can be used to identify phishing websites using URL and webpage features. The paper discusses common machine learning models such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine...

Neuro-Symbolic Reasoning with Hierarchical Bayesian Networks

This paper proposes a novel neuro-symbolic reasoning framework based on Hierarchical Bayesian Networks (HBNs). The core challenge in neuro-symbolic reasoning lies in effectively integrating the pattern recognition capabilities of neural networks with the structured reasoning capabilities of symbolic systems. Traditiona...

Explainable AI for System Security

This paper investigates the application of Explainable Artificial Intelligence (XAI) to enhance system security. Traditional security systems often rely on opaque "black box" AI models, hindering effective threat detection and defense. This work proposes a system architecture leveraging XAI techniques to provide interp...

Explainable AI via Causal Bayesian Networks

Current Explainable AI (XAI) techniques frequently generate explanations that are merely post-hoc justifications for model predictions, lacking a deep understanding of the model's reasoning process. This paper proposes a novel approach to XAI based on the construction of Causal Bayesian Networks (CBNs). CBNs are employ...

k-jinma/dogo-tutor: dogo-tutor v0.0.2 - Initial Release

This is the initial release of "dogo-tutor," an AI tutor designed for coding education. This release is created to archive the repository and obtain a DOI via Zenodo. Key Features Provides step-by-step hints (problem clarification, strategy, logic structuring, and partial code) rather than direct answers. Designed to o...

A Structural Framework for AI Ethics and Predictability: Re-centering Alignment on Stable Otherness

Coexisting safely with unpredictable Artificial Intelligence (AI) remains a foundational challenge for contemporary AI ethics. This paper proposes “stable otherness” as a relational framework that re-centers alignment on sociotechnical structures. Rather than treating AI as an autonomous conscious agent, we define AI a...

Autonomous AI-Native Digital Twins for Mission-Critical Data Centres: A Multi-Layer Framework for Predictive Commissioning, Operational Readiness, Resilience and Lifecycle Optimization

The rapid convergence of artificial intelligence (AI) workloads, high-density computing, advanced liquid and air cooling, increasingly distributed electrical architectures, and stringent availability requirements is transforming the data centre from a predominantly passive infrastructure environment into a highly dynam...