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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 181190 of 971 papers

TitleStatusHype
Coherent Local Explanations for Mathematical OptimizationCode0
CohEx: A Generalized Framework for Cohort ExplanationCode0
Applying Genetic Programming to Improve Interpretability in Machine Learning ModelsCode0
CARE: Coherent Actionable Recourse based on Sound Counterfactual ExplanationsCode0
EXPLAN: Explaining Black-box Classifiers using Adaptive Neighborhood GenerationCode0
Communicating Smartly in the Molecular Domain: Neural Networks in the Internet of Bio-Nano ThingsCode0
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in SenegalCode0
Evaluating saliency methods on artificial data with different background typesCode0
FaceX: Understanding Face Attribute Classifiers through Summary Model ExplanationsCode0
Explainability in Music Recommender SystemsCode0
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