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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 191200 of 971 papers

TitleStatusHype
Multi-SpaCE: Multi-Objective Subsequence-based Sparse Counterfactual Explanations for Multivariate Time Series ClassificationCode0
Assessing high-order effects in feature importance via predictability decomposition0
REPEAT: Improving Uncertainty Estimation in Representation Learning ExplainabilityCode0
Discrete Subgraph Sampling for Interpretable Graph based Visual Question AnsweringCode0
FaceX: Understanding Face Attribute Classifiers through Summary Model ExplanationsCode0
Neural network interpretability with layer-wise relevance propagation: novel techniques for neuron selection and visualization0
From Flexibility to Manipulation: The Slippery Slope of XAI EvaluationCode0
A Unified Framework for Evaluating the Effectiveness and Enhancing the Transparency of Explainable AI Methods in Real-World Applications0
OMENN: One Matrix to Explain Neural Networks0
Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos0
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