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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 211220 of 971 papers

TitleStatusHype
Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscienceCode0
EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust ModelsCode0
CoXQL: A Dataset for Parsing Explanation Requests in Conversational XAI SystemsCode0
iPDP: On Partial Dependence Plots in Dynamic Modeling ScenariosCode0
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in SenegalCode0
A novel approach to generate datasets with XAI ground truth to evaluate image modelsCode0
Data-Adaptive Discriminative Feature Localization with Statistically Guaranteed InterpretationCode0
Analyzing and Improving the Robustness of Tabular Classifiers using Counterfactual ExplanationsCode0
Leveraging CAM Algorithms for Explaining Medical Semantic SegmentationCode0
Bounded logit attention: Learning to explain image classifiersCode0
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