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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 251260 of 971 papers

TitleStatusHype
Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful ModelsCode0
Space-scale Exploration of the Poor Reliability of Deep Learning Models: the Case of the Remote Sensing of Rooftop Photovoltaic SystemsCode0
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in SenegalCode0
T5 for Hate Speech, Augmented Data and EnsembleCode0
An Ontology-Enabled Approach For User-Centered and Knowledge-Enabled Explanations of AI SystemsCode0
Explainability of Machine Learning Models under Missing DataCode0
Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree EnsemblesCode0
An Interpretable Deep Learning Approach for Skin Cancer CategorizationCode0
Explainability in Music Recommender SystemsCode0
Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?Code0
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