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explainable ai

2,123 papers

#explainable ai Open access Oct 2026

A comprehensive review of AI-driven water-quality monitoring and prediction: advances, challenges, and future directions

Water quality has emerged as a critical global concern that requires advanced monitoring and management strategies. Traditional water-quality assessment methods predominantly rely on laboratory-oriented analysis that are time consuming, expensive, and are often labor-intensive. This study presents a thorough overview o...

K. Yukesh Kumar, P. Kumaresan · 0 citations
#explainable ai Book Oct 2026

The Fourth Blow

The chapter examines the effects of digitalization and artificial intelligence (AI) on human experience, understanding and healing. It compares the associated developments with the three major historical blows to humanity caused by scientific discoveries: the Copernican revolution, Darwin&s;s theory of evolution, and F...

Martin Herberhold · 0 citations
#explainable ai Open access Oct 2026

Impact of AI-Based Fraud Prevention on Customer Trust in Digital Payment Systems: An Empirical Investigation of Continuance Intention among Indian Users

Abstract The rapid expansion of digital payment ecosystems in India, anchored by the Unified Payments Interface (UPI), has been accompanied by a sharp rise in payment fraud, eroding consumer confidence and threatening long-term adoption. Artificial intelligence (AI)-driven fraud prevention mechanisms—real-time anomaly...

Dr. Devanjali Dutta · 0 citations
#explainable ai Open access Oct 2026

PREreview of "What AI Benchmarks Actually Measure: Adapting Convergent and Discriminant Validity to Interrogate Fifty-Six AI Benchmarks"

This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/23180288. ## Summary Benchmark scores shape which models get funded, bought, and regulated — yet whether benchmarks measure what they claim to measure is rarely tested. This paper i...

Karmendra Pandey · 0 citations
#explainable ai Open access Oct 2026

Can We Teach AI to See the World the Way Animals Do?

Tweet For decades, ecology has been very good at explaining the past. We can look at a collapsed fishery, a vanished pollinator population, or a coral reef bleaching event and piece together, after the fact, what went wrong. What ecology has struggled to do, and what it needs to get better at, is predicting these chang...

Pensoft Editorial Team · 0 citations
#explainable ai Oct 2026

Consumption value in GenAI travel photography: a mixed-methods study from an affordance–constraint perspective

Purpose This study develops an affordance–constraint–value framework to explain how GenAI travel photography shapes consumption value and behavioral intentions. It aims to examine how perceived affordances and constraints relate to functional, emotional and social value, and how these value dimensions influence continu...

Xuejie Qiu, Fei Hao, Shuxu Liu et al. · 0 citations
#explainable ai Open access Oct 2026

What a Crossref record shows when a paper is retracted: one month of retraction deposits (v3)

# What a Crossref record shows when a paper is retracted **A field-level audit of one month of retraction deposits (v3)** Author: Trafalgar Law (independent). Data pulled from the public Crossref REST API on 2026-09-23. Sample and reproduction query included. This is a metadata audit, not an accusation against any publ...

Trafalgar Law · 0 citations
#explainable ai Open access Oct 2026

Explainable artificial intelligence for secure and privacy-preserving 5 G networks

Explainable artificial intelligence (XAI) is increasingly relevant to the security, privacy, and governance of AI-enabled 5 G and emerging 6 G networks. However, existing studies remain fragmented across wireless tasks, data modalities, network planes, and evaluation criteria, making it difficult to determine when an e...

Qiuyue Liao, Yue Chen, Shuangjiang He et al. · 0 citations
#explainable ai Oct 2026

Artificial intelligence adoption and green collaborative innovation: knowledge-based channels and governance conditions

Purpose This study aims to examine whether and how firm-level artificial intelligence (AI) adoption promotes green collaborative innovation. It focuses on two knowledge-based capability channels, namely, knowledge learning capability and green governance capability, and investigates the organizational and institutional...

Yang Xu, Qilong Zhou, Qi Fan et al. · 0 citations
#explainable ai Open access Oct 2026

Agency SEO Reseller Fulfillment Planning Guide

This educational guide explains how agencies and web designers can evaluate an outsourced SEO fulfillment model before introducing it to clients. It covers discovery, scope definition, communication ownership, branding choices, reporting, traditional and AI search visibility, quality controls, and client handoff practi...

SEO Leads, SEO Leads · 0 citations
#explainable ai Open access Oct 2026

O'ZBEKISTONDA KREDIT RISKLARINI BOSHQARISHDA SUN'IY INTELLEKT TEXNOLOGIYALARIDAN FOYDALANISH MEXANIZMLARINI TAKOMILLASHTIRISH

Mazkur tezis O'zbekiston tijorat banklarida kredit risklarini baholash va boshqarish jarayonini sun'iy intellekt texnologiyalari asosida takomillashtirishga bag'ishlangan. Tadqiqotda kredit portfeli kengayishi sharoitida qarz oluvchining defolt ehtimolini oldindan baholash, an'anaviy skoringni ma'lumotlarga asoslangan...

Umurqulova Rushana Aziz qizi · 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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