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Explainable artificial intelligence

XAI refers to methods and techniques in the application of artificial intelligence (AI) such that the results of the solution can be understood by humans. It contrasts with the concept of the "black box" in machine learning where even its designers cannot explain why an AI arrived at a specific decision. XAI may be an implementation of the social right to explanation. XAI is relevant even if there is no legal right or regulatory requirement—for example, XAI can improve the user experience of a product or service by helping end users trust that the AI is making good decisions. This way the aim of XAI is to explain what has been done, what is done right now, what will be done next and unveil the information the actions are based on. These characteristics make it possible (i) to confirm existing knowledge (ii) to challenge existing knowledge and (iii) to generate new assumptions.

Papers

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Showing 201–250 of 971 papers

TitleStatusHype
Forma mentis networks predict creativity ratings of short texts via interpretable artificial intelligence in human and GPT-simulated raters—0
Explaining the Impact of Training on Vision Models via Activation Clustering—0
XAI and Android Malware Models—0
Guarding the Gate: ConceptGuard Battles Concept-Level Backdoors in Concept Bottleneck Models—0
Leveraging Gene Expression Data and Explainable Machine Learning for Enhanced Early Detection of Type 2 Diabetes—0
Adapting the Biological SSVEP Response to Artificial Neural Networks—0
Explainable Artificial Intelligence for Medical Applications: A Review—0
X-DFS: Explainable Artificial Intelligence Guided Design-for-Security Solution Space Exploration—0
BayesNAM: Leveraging Inconsistency for Reliable Explanations—0
Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review—0
Visually Analyze SHAP Plots to Diagnose Misclassifications in ML-based Intrusion Detection—0
Causal Discovery and Classification Using Lempel-Ziv ComplexityCode0
Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing—0
Explainable Artificial Intelligence for Dependent Features: Additive Effects of CollinearityCode0
FNDEX: Fake News and Doxxing Detection with Explainable AI—0
On the Black-box Explainability of Object Detection Models for Safe and Trustworthy Industrial ApplicationsCode0
Few-Shot Multimodal Explanation for Visual Question AnsweringCode0
Info-CELS: Informative Saliency Map Guided Counterfactual Explanation—0
AI Readiness in Healthcare through Storytelling XAI—0
An Ontology-Enabled Approach For User-Centered and Knowledge-Enabled Explanations of AI SystemsCode0
Trustworthy XAI and Application—0
XAI-FUNGI: Dataset resulting from the user study on comprehensibility of explainable AI algorithms—0
User-centric evaluation of explainability of AI with and for humans: a comprehensive empirical study—0
Formal Explanations for Neuro-Symbolic AI—0
CohEx: A Generalized Framework for Cohort ExplanationCode0
Explainable AI Methods for Multi-Omics Analysis: A Survey—0
Information Importance-Aware Defense against Adversarial Attack for Automatic Modulation Classification:An XAI-Based Approach—0
Study on the Helpfulness of Explainable Artificial IntelligenceCode0
Natural Language Counterfactual Explanations for Graphs Using Large Language ModelsCode0
CAT: Concept-level backdoor ATtacks for Concept Bottleneck Models—0
Looking into Concept Explanation Methods for Diabetic Retinopathy ClassificationCode0
Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks—0
Easydiagnos: a framework for accurate feature selection for automatic diagnosis in smart healthcare—0
Tackling the Accuracy-Interpretability Trade-off in a Hierarchy of Machine Learning Models for the Prediction of Extreme HeatwavesCode0
Leveraging CAM Algorithms for Explaining Medical Semantic SegmentationCode0
Developing Guidelines for Functionally-Grounded Evaluation of Explainable Artificial Intelligence using Tabular Data—0
Examining the Rat in the Tunnel: Interpretable Multi-Label Classification of Tor-based Malware—0
Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution—0
From Pixels to Words: Leveraging Explainability in Face Recognition through Interactive Natural Language Processing—0
Deep Learning for Precision Agriculture: Post-Spraying Evaluation and Deposition EstimationCode0
Statistical tuning of artificial neural network—0
Explainable AI needs formal notions of explanation correctness—0
Counterfactual Explanations for Clustering Models—0
Additive-feature-attribution methods: a review on explainable artificial intelligence for fluid dynamics and heat transfer—0
Cartan moving frames and the data manifoldsCode0
Harnessing AI data-driven global weather models for climate attribution: An analysis of the 2017 Oroville Dam extreme atmospheric riverCode0
Global Lightning-Ignited Wildfires Prediction and Climate Change Projections based on Explainable Machine Learning Models—0
Deep Learning for predicting rate-induced tipping—0
Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges—0
Discovering Cyclists' Visual Preferences Through Shared Bike Trajectories and Street View Images Using Inverse Reinforcement LearningCode0
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