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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 581590 of 971 papers

TitleStatusHype
Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection0
Data-Adaptive Discriminative Feature Localization with Statistically Guaranteed InterpretationCode0
SolderNet: Towards Trustworthy Visual Inspection of Solder Joints in Electronics Manufacturing Using Explainable Artificial Intelligence0
Using explainability to design physics-aware CNNs for solving subsurface inverse problems0
Explainable Artificial Intelligence in Construction: The Content, Context, Process, Outcome Evaluation Framework0
Explainable Artificial Intelligence: Precepts, Methods, and Opportunities for Research in Construction0
REVEL Framework to measure Local Linear Explanations for black-box models: Deep Learning Image Classification case of studyCode0
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in SenegalCode0
Motif-guided Time Series Counterfactual Explanations0
Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions0
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