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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 771780 of 971 papers

TitleStatusHype
Explainability in Music Recommender SystemsCode0
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in SenegalCode0
An explainable three dimension framework to uncover learning patterns: A unified look in variable sulci recognitionCode0
SESS: Saliency Enhancing with Scaling and SlidingCode0
Explainability of Machine Learning Models under Missing DataCode0
Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?Code0
Generative and Explainable AI for High-Dimensional Channel EstimationCode0
Shapelet-Based Counterfactual Explanations for Multivariate Time SeriesCode0
Multi-modal volumetric concept activation to explain detection and classification of metastatic prostate cancer on PSMA-PET/CTCode0
Multi-SpaCE: Multi-Objective Subsequence-based Sparse Counterfactual Explanations for Multivariate Time Series ClassificationCode0
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