explainable ai

Transforming Chemical Safety Policy in the AI Era: A Seven-Action Strategy for the Five Chemical Safety Acts Under the Ministry of Climate, Energy, and Environment

Chemical safety in the Republic of Korea is governed by five acts falling under the purview of the Ministry of Climate, Energy, and Environment (MCEE): the Act on Registration and Evaluation of Chemicals (K-REACH Act), the Chemicals Control Act (CCA), the Environmental Health Act, the Environmental Damage Relief Act, a...

Advancing explainable prognostics and health management: Insights from new run-to-failure data of the Tennessee Eastman Process

The construction of health indicators (HIs) for failure prognostics is crucial for monitoring and predicting the health state of multi-component systems. Recently, data-driven methods for HI construction and prognostics have gained increasing attention, both relying heavily on high-quality run-to-failure (RTF) data for...

A Survey on the Use of Machine Learning within Computer Algebra

This survey explores the use of Machine Learning (ML) in the field of Computer Algebra (CA), both to optimise existing CA algorithms, and to perform symbolic computation directly. Traditional symbolic methods, while mathematically rigorous, often are computationally expensive thus limiting their use in real-world appli...

Explainable AI–enhanced pavement deterioration modeling using hybrid classification–regression deep learning architecture

Abstract Effective Pavement Management System (PMS) planning depends on the ability to anticipate both the severity and physical extent of multiple distress types under real-world data constraints. This work introduces an interpretable dual-stage Deep Learning (DL) framework that sequentially classifies distress severi...

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...

AEGIS Supplemental Material A: Technical Appendix to AEGIS: A Portable Evidence Interface Between AI-Agent Logging Duties and Independent Audit

This independently citable appendix contains specification and deployment detail for the AEGIS v1 interoperability profile: the complete design-principle catalog, architecture and adapter tables, extension configurations, split-view case analysis, byte-level field semantics, security assumptions, and verification inter...

A Comprehensive Study of Machine Learning and Deep Learning for Heart Disease Prediction

The problems of this paper are the problems of this paper, and some future directions for constructing a reliable cardiac disease prediction system with clinical applications are proposed.

Machine Intuition

Can an artificial intelligence system select the right action before it can articulate a valid reason for that action? Addressing this question at the intersection of reasoning, interpretability, agent safety, and latent computation, we develop a non-anthropomorphic definition of machine intuition as pre-explanatory co...

Opening the Black Box a Crack: A Historical Development Review of Explainability and Interpretability of Black-Box Models

This article presents a narrative review of Explainability and Interpretability of Black-Box Models in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an...

Opening the Black Box a Crack: A Historical Development Review of Explainability and Interpretability of Black-Box Models

This article presents a narrative review of Explainability and Interpretability of Black-Box Models in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an...

Machine Intuition

Can an artificial intelligence system select the right action before it can articulate a valid reason for that action? Addressing this question at the intersection of reasoning, interpretability, agent safety, and latent computation, we develop a non-anthropomorphic definition of machine intuition as pre-explanatory co...

PATTERN-X: Guiding sustainable innovation in intelligent lighting systems through an explainable AI framework for technology convergence forecasting