Tagged articles

explainable AI

29 articles · Page 1 of 1

Aug 12, 2026 · Artificial Intelligence

Four Transformative Leaps in 2026 Model Evaluation That Disrupt Traditional Paradigms

The article analyzes how 2026 model evaluation shifts from static accuracy metrics to dynamic resilience testing, causal‑provenance data, multi‑dimensional explainable credentials, and collaborative governance, citing real‑world case studies that demonstrate dramatic reductions in failure rates and regulatory friction.

AI resilienceModel Evaluationcausal provenance dataset

0 likes · 7 min read

Four Transformative Leaps in 2026 Model Evaluation That Disrupt Traditional Paradigms

Machine Heart

Aug 5, 2026 · Artificial Intelligence

Atomic Dance: Explainable Music-to-Dance Generation Using Atomic Movements

The paper introduces Atomic Dance, a two‑stage framework that first discovers repeatable, semantically labeled atomic movements and then plans and completes dance sequences, achieving more coherent, rhythm‑aligned and editable music‑driven choreography, as demonstrated on the AIST++ benchmark.

Atomic MovementsChoreographyECCV 2026

0 likes · 9 min read

Atomic Dance: Explainable Music-to-Dance Generation Using Atomic Movements

Machine Heart

Jul 10, 2026 · Information Security

Detect Insider Agents in Multi-Agent Networks with XG-Guard’s Explainable GAD

XG-Guard introduces a novel unsupervised graph anomaly detection framework that jointly encodes sentence- and token-level features of LLM agents, leverages theme-based anomaly scoring and covariance-based score fusion to pinpoint malicious agents in multi-agent systems, providing fine-grained explanations and enabling automatic communication isolation.

LLM securityUnsupervised Learningexplainable AI

0 likes · 9 min read

Detect Insider Agents in Multi-Agent Networks with XG-Guard’s Explainable GAD

AI Large-Model Wave and Transformation Guide

Jun 8, 2026 · Artificial Intelligence

Designing a High‑Reliability Cognitive Reasoning System with Ontology‑Based Architecture

The article presents a detailed architecture for a high‑reliability cognitive reasoning system that combines logical inference, semantic constraints, and a seven‑layer defense to achieve efficient deduction and strict error prevention across critical domains such as medical diagnosis and financial risk control.

Knowledge Graphcognitive reasoningexplainable AI

0 likes · 6 min read

Designing a High‑Reliability Cognitive Reasoning System with Ontology‑Based Architecture

Woodpecker Software Testing

May 14, 2026 · Artificial Intelligence

Why AI Is Harder to Test and How to Build Robust Security Pipelines

As AI moves into finance, healthcare, and autonomous driving, real incidents expose the limits of traditional testing, prompting a shift toward AI security testing that tackles exploding input spaces, untraceable logic, and runtime drift through adversarial robustness, fairness audits, jailbreak checks, and supply‑chain verification, all integrated into CI/CD pipelines.

AI security testingCI/CD integrationadversarial robustness

0 likes · 8 min read

Why AI Is Harder to Test and How to Build Robust Security Pipelines

Machine Learning Algorithms & Natural Language Processing

Apr 16, 2026 · Artificial Intelligence

Evidence Mining for Explainable AI: Methods and Applications

The talk introduces evidence‑mining techniques that extract supporting information from input text to improve model explainability, discusses the shortcut‑learning pitfalls of existing methods, and presents a new approach that enhances reliability and integrates with large‑model chain‑of‑thought compression for more interpretable, efficient reasoning.

AI researchLarge Language Modelsevidence mining

0 likes · 4 min read

Evidence Mining for Explainable AI: Methods and Applications

Woodpecker Software Testing

Mar 15, 2026 · Artificial Intelligence

Why 95% of AI Models Fail: A Deep Dive into Model Evaluation Techniques

The article explains that a high‑accuracy model alone does not guarantee a deployable AI system; it details how inadequate evaluation leads to most production failures and presents a comprehensive, multi‑dimensional evaluation framework—including distributional robustness, fairness, explainability, temporal stability, and efficiency trade‑offs—plus practical CI/CD pipelines and common pitfalls.

AI quality assuranceCI/CDModel Evaluation

0 likes · 7 min read

Why 95% of AI Models Fail: A Deep Dive into Model Evaluation Techniques

AI Frontier Lectures

Feb 10, 2026 · Artificial Intelligence

Can an 8B Model Outperform GPT‑4 in Faithfulness Detection? Inside FaithLens

FaithLens is an 8‑billion‑parameter model that surpasses GPT‑4.1 and other large models on twelve hallucination‑detection benchmarks while providing high‑quality natural‑language explanations, thanks to a novel data‑synthesis pipeline, three‑dimensional filtering, and rule‑based reinforcement learning.

LLM hallucinationefficient inferenceexplainable AI

0 likes · 12 min read

Can an 8B Model Outperform GPT‑4 in Faithfulness Detection? Inside FaithLens

Kuaishou Tech

Jan 28, 2026 · Artificial Intelligence

BLM‑Guard: Explainable Multimodal Ad Moderation Using Chain‑of‑Thought and Policy‑Aligned RL

The paper introduces BLM‑Guard, an explainable multimodal ad‑moderation framework that combines interleaved‑modal chain‑of‑thought reasoning with a policy‑aligned reinforcement‑learning reward to detect hidden cross‑modal violations in short‑video ads, and presents a new benchmark that demonstrates state‑of‑the‑art performance across multiple risk scenarios.

ad risk detectionbenchmarkchain-of-thought

0 likes · 12 min read

BLM‑Guard: Explainable Multimodal Ad Moderation Using Chain‑of‑Thought and Policy‑Aligned RL

HyperAI Super Neural

Nov 20, 2025 · Artificial Intelligence

From 9,874 Papers to 15,000 Structures: MOF‑ChemUnity Rebuilds MOF Knowledge for Explainable AI

MOF‑ChemUnity constructs a scalable, extensible knowledge graph that links millions of MOF names and synonyms to over 15,000 crystal structures using LLM‑driven entity matching, enabling accurate, explainable AI‑assisted material discovery, water‑stability prediction, expert recommendation validation, and graph‑enhanced retrieval across diverse applications.

Knowledge GraphMOFMaterials Discovery

0 likes · 17 min read

From 9,874 Papers to 15,000 Structures: MOF‑ChemUnity Rebuilds MOF Knowledge for Explainable AI

Architecture & Thinking

Sep 12, 2025 · Artificial Intelligence

How Knowledge Graphs Turn Large Language Models into Trustworthy Experts

Integrating structured knowledge graphs with generative AI provides traceable, explainable, and high‑precision reasoning across domains such as medicine, finance, and law, through techniques like Retrieval‑Augmented Generation, graph neural networks, and adaptive planning, dramatically reducing hallucinations and boosting expert‑level performance.

AI hallucinationGraph Neural NetworkKnowledge Graph

0 likes · 12 min read

How Knowledge Graphs Turn Large Language Models into Trustworthy Experts

AIWalker

Jun 24, 2025 · Artificial Intelligence

How Multimodal Fusion Accelerates Paper Publication: Key Insights and Resources

The article surveys 117 recent multimodal‑fusion papers, classifies them into improvement‑based and combination‑based approaches, highlights representative works such as TimeXL, OGP‑Net, MMR‑Mamba and FusionSight, and provides a free collection of papers, classic models and code repositories for researchers.

AI researchMultimodal Fusioncomputer vision

0 likes · 8 min read

How Multimodal Fusion Accelerates Paper Publication: Key Insights and Resources

21CTO

Nov 17, 2024 · Artificial Intelligence

Why Large Language Models Threaten Component‑Based Software Development

Software developers must confront the challenges of large language models, which lack composable, testable components and transparency, raising issues of explainability, safety, legal ownership, and sustainability, and the article proposes building testable, interchangeable components to enable truly explainable AI.

AI ethicscomponent testingexplainable AI

0 likes · 8 min read

Why Large Language Models Threaten Component‑Based Software Development

JD Retail Technology

Feb 26, 2024 · Artificial Intelligence

Explainable AI Forecasting and End-to-End Inventory Management in JD's Smart Supply Chain

The article details JD’s smart supply‑chain innovations, describing an explainable AI forecasting method that boosts prediction accuracy while maintaining interpretability, and an end‑to‑end inventory management model based on multi‑quantile RNNs that improves replenishment decisions, reduces costs, and enhances overall operational efficiency.

explainable AIforecastinginventory management

0 likes · 14 min read

Explainable AI Forecasting and End-to-End Inventory Management in JD's Smart Supply Chain

Baidu Tech Salon

Dec 14, 2023 · Artificial Intelligence

Baidu Research Institute 2023 Paper Sharing Session – Presented Papers Overview

The Baidu Research Institute’s 2023 Paper Sharing Session featured eight cutting‑edge papers—from semi‑supervised web‑search ranking and hierarchical reinforcement learning for autonomous intersections to spatial‑heterophily graph networks, a unified XAI benchmark, differentiable neuro‑symbolic KG reasoning, and novel stochastic‑gradient and neural‑field loss analyses—showcasing advances across AI, data mining, and computer vision.

Artificial IntelligenceAutonomous VehiclesNeural Fields

0 likes · 10 min read

Baidu Research Institute 2023 Paper Sharing Session – Presented Papers Overview

AsiaInfo Technology: New Tech Exploration

Oct 20, 2023 · Operations

How Transparent AI Boosts Trust in AIOps: Explainable Root‑Cause Solutions

This article examines the rapid growth of the Chinese IT operations market, explains why AIOps faces trust challenges due to opaque deep‑learning models, and presents AsiaInfo's transparent‑model and post‑hoc explanation engine together with three concrete explainable root‑cause analysis methods, concluding with future outlooks for trustworthy AIOps.

AI TrustAIOpsOperations

0 likes · 13 min read

How Transparent AI Boosts Trust in AIOps: Explainable Root‑Cause Solutions

Rare Earth Juejin Tech Community

Aug 6, 2023 · Artificial Intelligence

Explaining Image Recognition: Logistic Regression and Convolutional Neural Networks

This article introduces the principles of image recognition, compares traditional logistic regression with convolutional neural networks, demonstrates their implementation using Python code, visualizes model weights, and explains key concepts such as padding, convolution, pooling, receptive fields, and multi‑layer feature extraction.

Logistic Regressionconvolutional neural networkexplainable AI

0 likes · 12 min read

Explaining Image Recognition: Logistic Regression and Convolutional Neural Networks

DataFunTalk

Aug 4, 2023 · Artificial Intelligence

Self‑Explaining Natural Language Models: Collaborative Game Rationalization and Solutions for Spurious Correlations

The article reviews the growing importance of model explainability in high‑risk domains, analyzes the challenges of large language models, introduces the collaborative game‑theoretic RNP framework, and presents three mitigation strategies—Folded Rationalization, Decoupled Rationalization, and Multi‑Generator Rationalization—along with experimental results and future research directions.

Collaborative RationalizationLipschitz ContinuitySelf-Explaining Models

0 likes · 15 min read

Self‑Explaining Natural Language Models: Collaborative Game Rationalization and Solutions for Spurious Correlations

DataFunTalk

Apr 28, 2023 · Artificial Intelligence

Causal Inference and Uplift Modeling for Insurance Recommendation and Explainability

This article explains how uplift sensitivity prediction, Bayesian causal networks, and decision‑path construction are applied to improve insurance product, coupon, and copy recommendations on the Fliggy platform, detailing modeling approaches, evaluation metrics, and practical outcomes of the causal inference framework.

AB testingBayesian networksInsurance Recommendation

0 likes · 16 min read

Causal Inference and Uplift Modeling for Insurance Recommendation and Explainability

Python Programming Learning Circle

Mar 21, 2023 · Artificial Intelligence

A Survey of 10 Python Libraries for Explainable AI (XAI)

This article introduces Explainable AI (XAI), outlines its importance, describes a step-by-step workflow, and reviews ten Python libraries—including SHAP, LIME, ELI5, Shapash, Anchors, BreakDown, Interpret‑Text, AI Explainability 360, OmniXAI, and XAI—providing usage examples and code snippets.

Pythonexplainable AImachine learning

0 likes · 12 min read

A Survey of 10 Python Libraries for Explainable AI (XAI)

DataFunTalk

Nov 4, 2022 · Artificial Intelligence

Explainable Knowledge Graph Reasoning: Background, Advances, Motivation, Recent Research, and Outlook

This article reviews explainable knowledge graph reasoning, covering its background, core concepts, downstream applications, major reasoning methods, motivations for interpretability, recent advances such as hierarchical and Bayesian reinforcement learning, meta‑path mining, and future research directions.

Knowledge Graphexplainable AIgraph reasoning

0 likes · 18 min read

Explainable Knowledge Graph Reasoning: Background, Advances, Motivation, Recent Research, and Outlook

Model Perspective

Oct 31, 2022 · Artificial Intelligence

Understanding SHAP: How Shapley Values Explain Black‑Box Models

This article explains the SHAP (Shapley Additive Explanation) method, its theoretical foundations in game theory, the computation of Shapley Values, various algorithmic approximations like TreeSHAP and DeepSHAP, practical code examples, and the strengths and limitations of using SHAP for model interpretability.

Model InterpretationSHAPShapley Values

0 likes · 11 min read

Understanding SHAP: How Shapley Values Explain Black‑Box Models

NetEase Yanxuan Technology Product Team

Aug 22, 2022 · Industry Insights

How We Built a Transparent, Explainable Traffic Allocation System for E‑Commerce

This article details the design and implementation of a transparent, explainable traffic‑decision system for an e‑commerce platform, covering background challenges, directional principles, selection and targeting methods, PV value estimation, allocation algorithms, and the supporting data‑engineering and visualization infrastructure.

PV estimationalgorithmic fairnesse-commerce

0 likes · 22 min read

How We Built a Transparent, Explainable Traffic Allocation System for E‑Commerce

AntTech

Jul 18, 2022 · Artificial Intelligence

Trusted AI Research at Ant Group: Advances in Computer Vision, Watermark Defense, Robust Machine Learning, and Explainable NLG

Ant Group’s security labs present a series of cutting‑edge AI research achievements—including hierarchical multi‑granular classification for computer vision, watermark‑vaccine defenses, multi‑modal document understanding, robust and explainable machine learning, and logic‑driven data‑to‑text generation—highlighting their commitment to trustworthy and secure AI applications.

AI safetyData2TextRobust Machine Learning

0 likes · 12 min read

Trusted AI Research at Ant Group: Advances in Computer Vision, Watermark Defense, Robust Machine Learning, and Explainable NLG

DataFunTalk

Jan 3, 2020 · Artificial Intelligence

Survey of Machine Learning Model Interpretability Techniques

This article provides a comprehensive survey of model interpretability in machine learning, covering its importance, evaluation criteria, and a wide range of techniques such as permutation importance, partial dependence plots, ICE, LIME, SHAP, RETAIN, and LRP, along with practical code examples and visualizations.

Feature ImportanceICELIME

0 likes · 39 min read

Survey of Machine Learning Model Interpretability Techniques

DataFunTalk

Feb 20, 2019 · Artificial Intelligence

Recommendation Reasoning and Its Path Toward Future AI

This article explores why recommendation systems need reasoning, how recommendation reasoning connects to future strong AI, discusses explainability, causal inference, graph-based reasoning, and the philosophical underpinnings of AI, while also reflecting on practical examples from Hulu's recommendation platform.

Future AIRecommendation Systemscausal reasoning

0 likes · 25 min read

Recommendation Reasoning and Its Path Toward Future AI

Alibaba Cloud Developer

Feb 13, 2019 · Artificial Intelligence

How Graph Neural Networks are Revolutionizing E‑commerce Recommendations

This article explores how cognitive computing combined with graph neural networks and text generation enables large‑scale interest mining, interpretable embeddings, and multi‑modal recommendation in e‑commerce, outlining platform implementations, explainable methods, and future directions for AI‑driven consumer engagement.

Recommendation Systemscognitive computingexplainable AI

0 likes · 9 min read

How Graph Neural Networks are Revolutionizing E‑commerce Recommendations

Hulu Beijing

Jun 8, 2018 · Artificial Intelligence

How Hulu Leverages AI for Video Recommendation, Content Understanding, and Ads

The article reviews Hulu’s 2018 iQIYI keynote on AI video applications, detailing how AI drives personalized recommendations, content analysis through computer vision and NLP, ad targeting across visual, linguistic, and semantic layers, and outlines the platform’s machine‑learning architecture and future directions.

AIHulucontent understanding

0 likes · 6 min read

How Hulu Leverages AI for Video Recommendation, Content Understanding, and Ads

JD Tech

May 24, 2018 · Artificial Intelligence

AI-Enabled Security: JD Security’s DEF CON China Presentation on Explainable AI for Security

JD Security showcased its AI‑driven security research at DEF CON China, presenting three accepted papers and a collaborative AI safety report with Penn State, detailing a black‑box explanation method using Gaussian‑mixture models to make deep‑learning decisions transparent for security applications.

AIDEF CONexplainable AI

0 likes · 9 min read

AI-Enabled Security: JD Security’s DEF CON China Presentation on Explainable AI for Security