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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 691700 of 971 papers

TitleStatusHype
Concept Embedding Analysis: A Review0
A Meta Survey of Quality Evaluation Criteria in Explanation Methods0
Multi-modal multi-objective model-based genetic programming to find multiple diverse high-quality models0
Explainable Artificial Intelligence for Exhaust Gas Temperature of Turbofan Engines0
Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanationsCode0
Optimizing Binary Decision Diagrams with MaxSAT for classification0
Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications0
An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrival0
Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement0
NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language TasksCode1
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