explainable ai

m-mdy-m/cdin: cdin v0.1.0-beta.7

[0.1.0-beta.7] — 2026-09-23 Largest release so far: 102 commits, 218 files changed, +21,760 / −149. Headline features are full UTF-8/RTL/Arabic shaping support, a theme system with 10 built-in themes, an optional-plugin system, a Lua test suite, Docker images, and a complete documentation website. The beta cycle contin...

Attitudes towards AI and the Demand for eXplainable AI: Does Spirituality Matter?

Conditions of Admission and Participation for People Testing and Raising AI Models. Specification v0.1

An open specification of conditions of admission and participation for people who test, evaluate and raise AI models. It covers declaration instead of prohibition of AI tools, a field for observations that cannot yet be named, deferred reports on what came of a decision, payment for agreed participation including negat...

Deteksi Cyberbullying pada Twitter menggunakan Distilbert dan (Explainable Ai) Shap

The prevalence of cyberbullying on Twitter demands a fast and transparent automated moderation system. This study designs an efficient and explainable binary cyberbullying detection system using DistilBERT and the SHAP-based Explainable AI (XAI) method. The dataset, sourced from Kaggle, consists of 13,169 raw data filt...

Designing a human-in-the-loop AI iterative process for research writing: a framework for preserving productive struggle

This Hypothesis and Theory paper addresses a design problem at the center of AI-assisted research writing: which cognitive demands should AI reduce, and which must remain the writer’s own work. Generative AI can absorb the effort of drafting, organizing, and synthesizing prose, but that same effort is where understandi...

AI-Assisted rational enzyme engineering: advancing biocatalysis for industrial applications

The emergence of artificial intelligence (AI) has initiated a paradigm shift in enzyme engineering. While traditional methods like directed evolution and rational design are proven, they often suffer from limited predictive power, scalability, and efficiency. The integration of AI, particularly machine learning and dee...

Development of an AI-Based Model for Predicting Cyber Attacks Using ML and Generative Techniques

Across the datasets tested, this hybrid, explainability-aware approach consistently outperformed conventional intrusion detection baselines on prediction accuracy, detection capability and adaptability to new attack types, positioning it as a scalable model for real-time attack prediction and security analysis in moder...

A Concrete Vision of the MMO Research Platform: A Knowledge-Generation Space Built around Research Islands, Discoverers, and a Permanent Conference

This paper builds on the preceding paper, "MMO Research Platform: A Concept for a Knowledge-Generation Space through Anonymous Collaboration and LLM Moderation," and develops its concrete spatial design, modes of participation, role structure, and operating principles. The previous paper identified a problem in modern...

Machine Learning and Explainable AI for Pain Assessment and Clinical Decision Support

This doctorate investigates the application of Virtual Reality (VR), Machine Learning (ML), and Explainable Artificial Intelligence (XAI) in pain management, pain assessment, and clinical decision support. Pain is a complex and multidimensional phenomenon that remains difficult to assess and manage effectively, particu...

AI Consciousness and Existential Risk

In AI, existential risk denotes the hypothetical threat posed by an artificial system that would possess both the capability and the objective, either directly or indirectly, to eradicate humanity. This issue is gaining prominence in scientific debate due to recent technical advancements and increased media coverage. I...

NV-Reason-CT: 3D Visual Language Model for CT Analysis

The NV-Reason-CT model, a generative vision--language model for chest and abdominal CT combining native 3D visual encoding with radiologist-guided reasoning, and the model and training code are released to support reproducible research on explainable AI for volumetric medical imaging.

Toward Clinically Interpretable Perioperative Decision Support: Explainable Machine Learning for 30-Day Postoperative Mortality Prediction.

This study developed and evaluated machine learning models using the novel Informative Surgical Patient dataset for Innovative Research Environment, a comprehensive perioperative dataset, to predict 30-day postoperative mortality and demonstrated the relevance of explainable machine learning for the identification of c...