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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 301310 of 971 papers

TitleStatusHype
Assessing the Efficacy of Deep Learning Approaches for Facial Expression Recognition in Individuals with Intellectual Disabilities0
Evaluating the Stability of Semantic Concept Representations in CNNs for Robust Explainability0
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
Evolutionary approaches to explainable machine learning0
Most General Explanations of Tree Ensembles (Extended Version)0
EVolutionary Independent DEtermiNistiC Explanation0
Evolved Explainable Classifications for Lymph Node Metastases0
Explainable AI-Based Interface System for Weather Forecasting Model0
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