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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 231240 of 971 papers

TitleStatusHype
nn2poly: An R Package for Converting Neural Networks into Interpretable Polynomials0
Weak Robust Compatibility Between Learning Algorithms and Counterfactual Explanation Generation Algorithms0
Enhancing Counterfactual Image Generation Using Mahalanobis Distance with Distribution Preferences in Feature Space0
Probabilities of Causation for Continuous and Vector Variables0
Unified Explanations in Machine Learning Models: A Perturbation ApproachCode0
Watermarking Counterfactual ExplanationsCode0
Locally Testing Model Detections for Semantic Global Concepts0
Less is More: Discovering Concise Network ExplanationsCode0
A Transformer variant for multi-step forecasting of water level and hydrometeorological sensitivity analysis based on explainable artificial intelligence technology0
Comparative Analysis of Hyperspectral Image Reconstruction Using Deep Learning for Agricultural and Biological Applications0
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