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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 161170 of 971 papers

TitleStatusHype
Cartan moving frames and the data manifoldsCode0
EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust ModelsCode0
Explainability in Music Recommender SystemsCode0
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex ModelsCode0
Explainability of Machine Learning Models under Missing DataCode0
CEnt: An Entropy-based Model-agnostic Explainability Framework to Contrast Classifiers' DecisionsCode0
Explainable Artificial Intelligence for Improved Modeling of ProcessesCode0
Challenges facing the explainability of age prediction models: case study for two modalitiesCode0
Explaining Deep Learning Models for Age-related Gait Classification based on time series accelerationCode0
Applying Genetic Programming to Improve Interpretability in Machine Learning ModelsCode0
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