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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 281290 of 971 papers

TitleStatusHype
End-to-end Stroke imaging analysis, using reservoir computing-based effective connectivity, and interpretable Artificial intelligenceCode0
Energy-based Model for Accurate Shapley Value Estimation in Interpretable Deep Learning Predictive ModelingCode0
Evaluating saliency methods on artificial data with different background typesCode0
Explainable Machine Learning for Breakdown Prediction in High Gradient RF CavitiesCode0
Does Dataset Complexity Matters for Model Explainers?Code0
Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and XAI EvaluationCode0
EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust ModelsCode0
Black Box Model Explanations and the Human Interpretability Expectations -- An Analysis in the Context of Homicide PredictionCode0
Explainability in Music Recommender SystemsCode0
Ensemble of Counterfactual ExplainersCode0
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