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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

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Showing 901–925 of 971 papers

TitleStatusHype
Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring—0
XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRPCode0
Explainability in Deep Reinforcement Learning—0
Survey of XAI in digital pathology—0
Explainable Artificial Intelligence Based Fault Diagnosis and Insight Harvesting for Steel Plates Manufacturing—0
Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence—0
Safety design concepts for statistical machine learning components toward accordance with functional safety standards—0
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 Intelligence—0
Am I Building a White Box Agent or Interpreting a Black Box Agent?—0
Drug discovery with explainable artificial intelligence—0
Using Deep Learning and Explainable Artificial Intelligence in Patients' Choices of Hospital Levels—0
Does Explainable Artificial Intelligence Improve Human Decision-Making?—0
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey—0
Explaining Local, Global, And Higher-Order Interactions In Deep LearningCode0
Explanations of Black-Box Model Predictions by Contextual Importance and UtilityCode0
Explainable Artificial Intelligence: a Systematic Review—0
A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation—0
Applying Genetic Programming to Improve Interpretability in Machine Learning ModelsCode0
Evolved Explainable Classifications for Lymph Node Metastases—0
Explainable Reinforcement Learning: A Survey—0
Post-hoc explanation of black-box classifiers using confident itemsets—0
A multi-component framework for the analysis and design of explainable artificial intelligence—0
LIMEtree: Consistent and Faithful Multi-class ExplanationsCode0
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