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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 861870 of 971 papers

TitleStatusHype
Interpretable Machine Learning for Survival AnalysisCode0
Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black BoxCode0
Interpretable ML for Imbalanced DataCode0
Interpretable Summaries of Black Box Incident Triaging with Subgroup DiscoveryCode0
Data-Adaptive Discriminative Feature Localization with Statistically Guaranteed InterpretationCode0
XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRPCode0
Interpreting End-to-End Deep Learning Models for Speech Source Localization Using Layer-wise Relevance PropagationCode0
midr: Learning from Black-Box Models by Maximum Interpretation DecompositionCode0
Intrinsic Subgraph Generation for Interpretable Graph based Visual Question AnsweringCode0
Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) methodCode0
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