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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 141150 of 971 papers

TitleStatusHype
Formal Explanations for Neuro-Symbolic AI0
CohEx: A Generalized Framework for Cohort ExplanationCode0
Information Importance-Aware Defense against Adversarial Attack for Automatic Modulation Classification:An XAI-Based Approach0
Explainable AI Methods for Multi-Omics Analysis: A Survey0
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 Models0
Looking into Concept Explanation Methods for Diabetic Retinopathy ClassificationCode0
Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks0
Explainable Earth Surface Forecasting under Extreme EventsCode1
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