explainable ai

Advancing Decoding Methods for Enhanced AI Model Performance and Interpretability

The rapid advancement of artificial intelligence, particularly in large language models, has brought significant challenges in decoding methods that balance performance with interpretability. This paper presents a comprehensive analysis of advanced decoding techniques that enhance both model capabilities and explainabi...

aiDIVA – hybrid AI for rare disease diagnostics using evidence-based, machine learning and language models

aiDIVA is presented, an ensemble-AI combining statistical and machine learning models trained on genomic and phenotypic data to identify causal variants among tens of thousands per patient, and applies a random forest model to classify pathogenicity and generates evidence-based scores for dominant and recessive disease...

AI as an Aviation Safety Decision Maker

Artificial intelligence is rapidly evolving from an analytical support technology into an active participant in aviation safety decision-making. This paper examines how Safety Management Systems (SMS) must evolve as AI increasingly recommends or executes safety-critical operational decisions within aircraft operations,...

Advanced multi-modal Threat Detection: Explainable AI for Early Warning from Human Behavioral Cues Advanced multi-modal Threat Detection Preprint

Autonomous Enterprise Platforms: A Framework for AI-Guided Decision Loops, Predictive Intelligence, and Continuous Organizational Adaptation

Enterprise platforms are evolving from systems that primarily record and analyze business operations into intelligent environments capable of predicting outcomes, recommending interventions, executing decisions, and learning from their consequences. This article proposes a conceptual Autonomous Enterprise Platform (AEP...

Exploring the Relationship Between Visit Frequency and Customer Retention in Study Cafes Using AI-Based Predictive Modeling

This study empirically investigates actual customer repurchase behavior by analyzing behavioral log and payment data collected from study cafe users in an attendance-based learning service environment. Unlike prior studies that primarily relied on survey-based measures, this study integrates attendance records, payment...

White-Box Completeness for Artificial Intelligence

Abstract Current research on explainable and white-box artificial intelligence faces prominent issues: conceptual disarray, divergent perspectives, and a disconnect between theory and practice. The foremost priority is therefore to return to the field’s purest objectives and explore its most fundamental questions. To t...

Understanding Human Acceptance of Explainable AI in Automated Aesthetic Evaluation: A Multi-Study Investigation of Advice Taking and Explanation Requirements

Explainable Artificial Intelligence (XAI) has been increasingly applied in image aesthetic quality evaluation. However, it remains unclear how perceived explainability influences users’ decision adoption and what specific explanation requirements shape this perception. This study develops and tests a multi-stage model...

Understanding Human Acceptance of Explainable AI in Automated Aesthetic Evaluation: A Multi-Study Investigation of Advice Taking and Explanation Requirements

Explainable Artificial Intelligence (XAI) has been increasingly applied in image aesthetic quality evaluation. However, it remains unclear how perceived explainability influences users’ decision adoption and what specific explanation requirements shape this perception. This study develops and tests a multi-stage model...

Can Artificial Intelligence Improve Work-Life Balance? Examining the Relationship between AI Adoption, Workload, Stress and Employee Well-Being

The growing use of Artificial Intelligence (AI) in organisations is changing how employees perform, organise, and experience their work. AI-supported systems can automate routine activities, assist with information processing and improve the speed of task completion. These advantages have created expectations that AI m...

Explainable Artificial Intelligence for Enhancing Intrusion Detection Systems: A Comprehensive Framework for Transparent Network Security

This research presents a comprehensive framework for integrating Explainable Artificial Intelligence (XAI) into Intrusion Detection Systems (IDS) to address the critical challenge of AI model opacity in cybersecurity. Traditional AI-based IDS models function as "black boxes," limiting trust, accountability, and practic...

Time Is One-Dimensional Because Prediction Demands It, Not Because a Law Says So ── The Same Procedure, With Only the Signature Changed, Gives Amplifications of 0.012 and 3.5 × 10^32 ── [Paper 253]

Why is time one-dimensional? The answer offered here is that no law decrees it; the requirement that one be able to predict allows nothing else. The equations of a universe with two times can be written, and their solutions exist. What breaks is prediction. No new mathematical theorem and no new law is claimed. Scope o...