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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 261270 of 971 papers

TitleStatusHype
Explainable Artificial Intelligence for Improved Modeling of ProcessesCode0
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in SenegalCode0
Explainability in Music Recommender SystemsCode0
Explainability of Machine Learning Models under Missing DataCode0
Do Protein Transformers Have Biological Intelligence?Code0
Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studiesCode0
midr: Learning from Black-Box Models by Maximum Interpretation DecompositionCode0
EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust ModelsCode0
EAG-RS: A Novel Explainability-guided ROI-Selection Framework for ASD Diagnosis via Inter-regional Relation LearningCode0
Evaluating saliency methods on artificial data with different background typesCode0
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