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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 901910 of 971 papers

TitleStatusHype
Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring0
XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRPCode0
Explainability in Deep Reinforcement Learning0
Survey of XAI in digital pathology0
Explainable Artificial Intelligence Based Fault Diagnosis and Insight Harvesting for Steel Plates Manufacturing0
Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence0
Safety design concepts for statistical machine learning components toward accordance with functional safety standards0
EXPLAN: Explaining Black-box Classifiers using Adaptive Neighborhood GenerationCode0
timeXplain -- A Framework for Explaining the Predictions of Time Series ClassifiersCode0
Locality Guided Neural Networks for Explainable Artificial Intelligence0
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