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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 281290 of 971 papers

TitleStatusHype
Bridging Human Concepts and Computer Vision for Explainable Face Verification0
A Novel Explainable Artificial Intelligence Model in Image Classification problem0
AgroXAI: Explainable AI-Driven Crop Recommendation System for Agriculture 4.00
Breaking Down Financial News Impact: A Novel AI Approach with Geometric Hypergraphs0
A Grounded Interaction Protocol for Explainable Artificial Intelligence0
Evolved Explainable Classifications for Lymph Node Metastases0
Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution0
Achieving Explainability for Plant Disease Classification with Disentangled Variational Autoencoders0
Enhancing Counterfactual Image Generation Using Mahalanobis Distance with Distribution Preferences in Feature Space0
A Novel Approach for Semiconductor Etching Process with Inductive Biases0
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