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explainable ai

2,395 papers

#federated learning Open access Sep 2026

Machine Learning-Based Insurance Claim Prediction Using Customer Behavioral and Policy Data: A Conceptual Framework for Intelligent Risk Assessment and Decision Support

A conceptual framework is proposed to illustrate the integration of customer behavioral characteristics, policy attributes, and advanced machine learning models into an intelligent decision-support system for claim prediction, highlighting the importance of explainable AI, continuous model monitoring, and human-in-the-...

Pragadeesh Roopchander · 0 citations
#graph neural networks Open access Sep 2026

Neuro-Symbolic Reasoning with Graph Neural Networks and Rule Extraction

This paper presents a novel framework for neuro-symbolic reasoning, addressing the longstanding challenge of integrating neural networks and symbolic reasoning. The core idea is to leverage the strengths of both approaches by employing a Graph Neural Network (GNN) to represent knowledge and perform initial reasoning st...

Jincheng Zhang · 0 citations
#generative ai Open access Sep 2026

Algorithmic Sovereignty in Digital Banking

Digital banks increasingly use machine learning and generative AI inside decisions that affect access to credit, fraud interventions, financial-crime controls, customer support and internal operations. The principal governance problem is not that regulators have demanded a single form of ‘explainable AI’. It is that a...

Vladislav Solodkiy · 0 citations
#generative ai Open access Sep 2026

One Technology, Divergent Outcomes: Extending Adaptive Structuration Theory to Explain Heterogeneous Organizational Responses to Generative AI

This paper explains why organizations that adopt the same generative AI/LLM technology end up with different results. It argues the real driver isn't the technology itself but "appropriation" — how employees actually use it day to day, which can differ from how it was intended. Using Adaptive Structuration Theory (DeSa...

Likhitha Macha · 0 citations
#explainable ai Review Open access Sep 2026

Artificial intelligence in physical and rehabilitation medicine: first update of the living systematic mapping review up to May 31st, 2025.

Literary trends of the update of the living systematic mapping review REHALISE up to May 31st, 2025, suggest a slight and gradual shift toward more clinically oriented applications, though robustness and reproducibility remain critical concerns.

Francesco Negrini, Calogero Malfitano, M. Valeri et al. · 0 citations
#artificial intelligence Open access Sep 2026

CAN YOU HEAR THE MUSIC - CHAPTER ( i ) - ( ( (( HMC-GATE )) ) ) - MUSIC AS THE COSMIC PRIMORDIAL CODE

MUSIC AS THE COSMIC PRIMORDIAL CODE The Feminine Essence and the Entangled Symphony of Existence ( ( (( HMC-GATE )) ) ) ARTISTIC-PHYLOSOPYCAL METACOGNITION ( APM- METACOGNITION ) AUTHORS NOTE : YOU ARE THE ESSENCE OF THE EXISTENCE when the universe, through the architecture of flesh and nerve, became capable of hearing...

Mohammad Ali Piran · 0 citations
#artificial intelligence Open access Sep 2026

Artificial intelligence literacy and readiness among neonatal nurses: a structural equation modeling study

Abstract Background Neonatal Intensive Care Units represent high-risk clinical environments where timely and accurate decision-making is critical for newborn survival, increasing the demand for advanced technological support. As artificial intelligence becomes progressively integrated into neonatal care, it is transfor...

Zübeyde Ezgi Erçelik, Diler YILMAZ, Merve Nur ERYILMAZ TAŞ · 0 citations
#artificial intelligence Open access Sep 2026

AI-Driven Sustainability in Industry 5.0: The Role of Responsible Leadership

As Industry 5.0 advances, artificial intelligence (AI) is increasingly positioned as a driver of sustainability in knowledge-based economies; however, empirical outcomes remain uneven and frequently symbolic. Addressing this paradox, this paper examines AI-driven sustainability through the lens of knowledge creation,...

Amlan Haque · 0 citations
#explainable ai Preprint Sep 2026

NeuroGraph: An AI Graph-Driven Neuro-Symbolic Framework for Explainable Threat Reasoning in Advanced Manufacturing

A graph-grounded neuro-symbolic framework that integrates ontology-aware symbolic query generation, knowledge graph retrieval, and neural language generation to support accurate and explainable threat analysis across information technology and operational technology environments is proposed.

Padmeswari Nandiya, Ahmad Mohsin, Ahmed Ibrahim et al. · 0 citations
#explainable ai Open access Sep 2026

Axiomatic Foundation for Explainable AI – Causal Inference as a Requirement

Current approaches to explainable AI (XAI) frequently fall short of providing genuinely understandable and trustworthy explanations. These methods often rely on post-hoc interpretations of black-box models, which can be misleading due to their failure to capture the underlying causal mechanisms driving predictions. Thi...

Jincheng Zhang · 0 citations

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GPT-Lab Oct 14, 2026

Building trustworthy AI: The role of AI harnesses, intent contracts and evals

AI is moving beyond impressive demos. After World Summit AI 2026 in Amsterdam, GPT-Lab's Muhammad Waseem reflects on what organizations need to make AI agents reliable, responsible and genuinely useful in everyday work. The post Building trustworthy AI: The role of AI harnesses, intent contracts and evals appeared first on GPT-Lab.

Microsoft Research Blog Oct 12, 2026

Simulating student learning for better AI tutoring

StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.

GPT-Lab Oct 12, 2026

AI Has Grown Up. Have We?

AI is moving beyond impressive demos. After World Summit AI 2026 in Amsterdam, GPT-Lab's Muhammad Waseem reflects on what organizations need to make AI agents reliable, responsible and genuinely useful in everyday work. The post AI Has Grown Up. Have We? appeared first on GPT-Lab.

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