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explainable ai

2,125 papers

#explainable ai Open access Oct 2026

Development and structural validation of the Artificial Intelligence Competency Scale for Nurse Educators (AI-CSNE): a three-domain measure of perceived AI competency

The rapid integration of artificial intelligence (AI) into healthcare and nursing education has created new competency requirements for nurse educators. Despite this growing demand, there is a notable lack of rigorously developed and psychometrically validated instruments specifically designed to assess AI-related comp...

Mona Gamal Mohamed, Zahra Abdirahman Mahmoud, Marwa Samir Sorour · 0 citations
#explainable ai Open access Oct 2026

The Intentional Field Ontology: Reality as directed potential, field-memory, and recursive intelligibility

This paper presents the Intentional Field Ontology, a philosophical and speculative scientific framework in which reality is understood not as a collection of primary objects but as one reality whose oneness is never still. Its ground is a single primitive with two inseparable aspects. Unity names the holding: the comp...

Eric Needham · 0 citations
#explainable ai Open access Oct 2026

Biomedical Electronics Explained: Medical Devices, Sensors & Healthcare Systems

Biomedical Electronics Explained: Medical Devices, Sensors & Healthcare Systems is a publication-grade Open Educational Resource (OER) module exploring the design, isolation, and clinical integration of biosensors, implantable devices, and diagnostic instrumentation. Serving as a specialized core module within the Elec...

Prep4Uni.Online · 0 citations
#explainable ai Open access Oct 2026

The Agentic Brain: Dual-Process Metacognition, Evidence-Gated Authority, and Runtime Governance

As AI agents move from producing answers to taking consequential actions, they must decide both how much reasoning to perform and what evidence justifies acting. We develop the agentic control plane, joining a System-1-like fast proposer, a System-2-like portfolio of deliberation and verification, a metacognitive route...

Fitih M. Cinnor · 0 citations
#explainable ai Open access Oct 2026

AI-Augmented Leadership: How Leaders Make Strategic Decisions Alongside AI

This thesis explores the paradigm of AI-augmented leadership, a new model of strategic decision-making that is emerging in the age of artificial intelligence. It argues that the role of the human leader is not being diminished by AI but is, in fact, becoming more critical and more complex. As AI takes on the tasks of o...

Kwan Hong Tan · 0 citations
#explainable ai Open access Oct 2026

amatya-aditya/obsidian-rss-dashboard: 2.7.0

RSS Dashboard 2.7.0 RSS Dashboard 2.7.0 brings your starred articles over from other feed readers, makes your data safer and easier to find, and gives you more control over which kinds of articles are protected from automatic cleanup. It also adds an image lightbox, a redesigned podcast episode list, new grouping optio...

Marc, Adiam, jonwilks et al. · 0 citations
#explainable ai Open access Oct 2026

AN ADAPTIVE HYBRID DEEP LEARNING FRAMEWORK FOR REAL-TIME CYBER THREAT AND ANOMALY DETECTION IN HIGH-THROUGHPUT NETWORK TRAFFIC

Modern enterprise digital infrastructures face unprecedented cyber threats characterized by high volume, zero-day vulnerabilities, and multi-stage attack vectors. Traditional rule-based Intrusion Detection Systems (IDS) and shallow machine learning models struggle to maintain high detection accuracy while minimizing fa...

Abduraimov Firdavsiy Alisher o'g'li, Valiyeva Nodiraxon Maxamatjonovna · 0 citations
#explainable ai Open access Oct 2026

PREreview of "Psychosexual Rehabilitation After Cancer: A Narrative Review of the Intervention Evidence and a Stepped-Care Model for Survivorship Practice"

This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/23121365. Summary of main findings and contribution This narrative review focuses on an important question: how psychosexual rehabilitation after cancer can be delivered at scale, r...

Ghadeer Asarwi · 0 citations
#explainable ai Open access Oct 2026

Understanding Consumer Behavior Through AI-Driven Predictive Analytics

Abstract: The rapid proliferation of artificial intelligence (AI) and big data analytics has fundamentally transformed how organizations understand, predict, and influence consumer behavior. Despite the growing adoption of AI-driven predictive analytics in marketing, there remains limited theoretical integration explai...

Dr. (Mrs). Vaishali Nadkarni · 0 citations
#explainable ai Open access Oct 2026

Structured PREreview of "Female Signatories and Peace Durability: Replicating Krause, Krause, and Bränfors (2018)"

This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/23120609. Does the introduction explain the objective of the research presented in the preprint? Yes Are the methods well-suited for this research? Somewhat appropriate A...

Khaiyam Khalid · 0 citations
#explainable ai Open access Oct 2026

FAIAS — Financial AI Independent Assurance Standard (v1.3.2)

FAIAS (Financial AI Independent Assurance Standard) is a baseline for reviewing generative, agentic and predictive AI systems in financial institutions. This is its first public release. What it contains A taxonomy of how these systems fail. 109 Independent Review Questions. Each has a review canvas and a test procedur...

Wonder Attah · 0 citations

From tech blogs

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Microsoft Research Blog Oct 7, 2026

Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses

Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.

Microsoft Research Blog Oct 6, 2026

What AI gets wrong and what failure teaches us

Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity.  The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.

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