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explainable ai

2,125 papers

#explainable ai Book Open access Oct 2026

THE EL-RAKHAWI DOCTRINE OF QUANTUM ALGORITHMIC JURISPRUDENCE: GOVERNING SUPERPOSITION, ENTANGLEMENT, AND HYBRID AI ON SILICON-QUANTUM SUBSTRATES

The El-Rakhawi Doctrine of Quantum Algorithmic Jurisprudence presents the first comprehensive legal framework bridging classical legal determinism and quantum computing. It addresses the paradigm shift where quantum AI operates on superposition, entanglement, and probabilistic outcomes. The doctrine introduces five fou...

mohamed kamal arafa el-rakhawi · 0 citations
#explainable ai Open access Oct 2026

PhytoDiagnostix — herramienta IA para la academia (UMH)

Banco de más de 50 microfotografías de Wikimedia Commons, cada una con su autoría y licencia. El código comprueba la respuesta del estudiante contra la etiqueta de la foto; la IA, opcional, explica sin cambiar el veredicto. El profesorado añade sus propias fotos con un JSON en el repositorio. A bank of 50+ Wikimedia Co...

Fernando Borrás · 0 citations
#explainable ai Open access Oct 2026

The Certificate and the Shield: Where ISO/IEC 42001 Meets the EU AI Act, and Where It Stops

A supplier holds a certificate, is proud of it, and attaches it to a customer's compliance questionnaire. The answer comes back in one line: that is not what we asked for. This whitepaper explains why, and what to do instead. ISO/IEC 42001 governs an organisation: how it decides what to build, who is accountable, how r...

Kris Cordier · 0 citations
#explainable ai Open access Oct 2026

AI Copyright Claims 2026: Model-Only Fails Human-Authorship Test

A 2026 U.S. copyright audit explains why prompts, model output and selection alone fail to establish human authorship in AI-generated passages, passage by passage. Independent technical note mirroring the canonical version: https://iprs.cloud/blog/ai-copyright-claims-2026-model-only-fails-human-authorship-test.php

Priya Menon · 0 citations
#explainable ai Open access Oct 2026

XAI Evaluation Framework

A modular, extensible framework for benchmarking explainable AI methods, including experiment orchestration, metric evaluation, statistical analysis, and reproducibility artifacts for tabular post-hoc explanation studies.

Jonathan Herrera-Vasquez · 0 citations
#explainable ai Open access Oct 2026

Signal Processing Explained: Systems, Analysis & Digital Communication Technologies

Signal Processing Explained: Systems, Analysis & Digital Communication Technologies is a publication-grade Open Educational Resource (OER) module covering the mathematical foundations, discrete-time systems, spectral transforms, and digital filter architectures governing signal analysis across electrical and electronic...

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

Dataset of Literature on Explainable Artificial Intelligence (XAI) in Education (2019–2026)

## Description This dataset contains a curated bibliometric collection of **118 peer-reviewed publications** indexation records from **Scopus** focusing on **Explainable Artificial Intelligence (XAI) in Education** published between **2019 and 2026**. The collection covers empirical studies, systematic mapping reviews,...

Carlos Enrique George-Reyes · 0 citations
#explainable ai Review Open access Oct 2026

A human-centred artificial intelligence and learning analytics framework for equitable personalised higher education enrolment

Artificial intelligence (AI) and learning analytics are increasingly deployed within higher education to support admissions decision-making, student success initiatives, and personalised learning. However, most AI-enabled admissions systems remain narrowly focused on efficiency, prediction, and institutional optimisati...

A. Alenezi · 0 citations
#explainable ai Open access Oct 2026

Sapiens in Control: A Six-Dimension Framework for Evaluating Control Failures in Advanced AI Agents

Sapiens in Control, formerly Human in Control, is the same independent public-interest research initiative and working-paper series. This paper proposes the Human Control Model: Intervention Durability, Technical Containment, Informed Delegation, Oversight Integrity, Control Contagion, and Situational Grounding. Versio...

Sapiens in Control Project · 0 citations
#explainable ai Open access Oct 2026

FROM HUMAN TO ALGORITHMS: Social and Legal Impact of Artificial Intelligence on Employment in India

"A machine can now hire a worker, rate a worker and remove a worker, but it cannot be asked why."Artificial intelligence is affecting Indian workers in two ways, and the law has dealt with only one of them.The first is job loss. The Economic Survey 2024-25, in its chapter "Labour in the AI Era: Crisis or Catalyst?",fou...

Dr. K. Prasanna Rani, Durshetty Pratibha · 0 citations
#explainable ai Open access Oct 2026

The ShowPapers Protocol: From Scattered Documents to Usable, Portable Records

The ShowPapers Protocol keeps original documents, structured details, linked notes, organization and review decisions together in a portable collection. ZIP carries the files; the protocol defines their records, relationships, provenance and proposed changes. This technical report describes specification 1.0.0, includi...

Nirbhay Pherwani, Ameya Kulkarni · 0 citations
#explainable ai Open access Oct 2026

PhytoDiagnostix — herramienta IA para la academia (UMH)

Banco de más de 50 microfotografías de Wikimedia Commons, cada una con su autoría y licencia. El código comprueba la respuesta del estudiante contra la etiqueta de la foto; la IA, opcional, explica sin cambiar el veredicto. El profesorado añade sus propias fotos con un JSON en el repositorio. A bank of 50+ Wikimedia Co...

Fernando Borrás, María E. García-Pastor · 0 citations

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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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