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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 171180 of 971 papers

TitleStatusHype
Interpreting Outliers in Time Series Data through Decoding Autoencoder0
Stacked ensemble\-based mutagenicity prediction model using multiple modalities with graph attention network0
Breaking Down Financial News Impact: A Novel AI Approach with Geometric Hypergraphs0
Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction0
Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between Features0
IBO: Inpainting-Based Occlusion to Enhance Explainable Artificial Intelligence Evaluation in HistopathologyCode0
Interactive dense pixel visualizations for time series and model attribution explanations0
Underwater SONAR Image Classification and Analysis using LIME-based Explainable Artificial IntelligenceCode0
VALE: A Multimodal Visual and Language Explanation Framework for Image Classifiers using eXplainable AI and Language ModelsCode0
OPTDTALS: Approximate Logic Synthesis via Optimal Decision Trees Approach0
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