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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 771780 of 971 papers

TitleStatusHype
A Deep Generative XAI Framework for Natural Language Inference Explanations Generation0
A Practical guide on Explainable AI Techniques applied on Biomedical use case applications0
Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning0
Exploring Explainable AI in the Financial Sector: Perspectives of Banks and Supervisory Authorities0
Explainable Artificial Intelligence for Smart City Application: A Secure and Trusted Platform0
ProtoShotXAI: Using Prototypical Few-Shot Architecture for Explainable AICode0
Automated Quality Control of Vacuum Insulated Glazing by Convolutional Neural Network Image Classification0
Classification of Viral Pneumonia X-ray Images with the Aucmedi Framework0
Advancing Nearest Neighbor Explanation-by-Example with Critical Classification Regions0
Intelligent Decision Assistance Versus Automated Decision-Making: Enhancing Knowledge Work Through Explainable Artificial Intelligence0
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