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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

Showing 126–150 of 971 papers

TitleStatusHype
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
Few-Shot Multimodal Explanation for Visual Question AnsweringCode0
On the Black-box Explainability of Object Detection Models for Safe and Trustworthy Industrial ApplicationsCode0
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
Information Importance-Aware Defense against Adversarial Attack for Automatic Modulation Classification:An XAI-Based Approach—0
Explainable AI Methods for Multi-Omics Analysis: A Survey—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
Explainable Earth Surface Forecasting under Extreme EventsCode1
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