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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 601–625 of 971 papers

TitleStatusHype
InFIP: An Explainable DNN Intellectual Property Protection Method based on Intrinsic Features—0
Machine Learning in Transaction Monitoring: The Prospect of xAI—0
Neuro-symbolic Explainable Artificial Intelligence Twin for Zero-touch IoE in Wireless Network—0
Toward the application of XAI methods in EEG-based systems—0
T5 for Hate Speech, Augmented Data and EnsembleCode0
Utilizing Explainable AI for improving the Performance of Neural Networks—0
Why Should I Choose You? AutoXAI: A Framework for Selecting and Tuning eXplainable AI SolutionsCode1
Fault Diagnosis using eXplainable AI: a Transfer Learning-based Approach for Rotating Machinery exploiting Augmented Synthetic Data—0
The Influence of Explainable Artificial Intelligence: Nudging Behaviour or Boosting Capability?—0
Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature InteractionsCode0
OAK4XAI: Model towards Out-Of-Box eXplainable Artificial Intelligence for Digital Agriculture—0
Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classificationCode0
Asset Pricing and Deep Learning—0
Enhancing Cluster Analysis With Explainable AI and Multidimensional Cluster PrototypesCode0
Survey on Deep Fuzzy Systems in regression applications: a view on interpretability—0
Responsibility: An Example-based Explainable AI approach via Training Process Inspection—0
Regulating eXplainable Artificial Intelligence (XAI) May Harm Consumers—0
Explainable Artificial Intelligence to Detect Image Spam Using Convolutional Neural Network—0
Incremental Permutation Feature Importance (iPFI): Towards Online Explanations on Data Streams—0
Generating detailed saliency maps using model-agnostic methods—0
Identifying Dominant Industrial Sectors in Market States of the S&P 500 Financial Data—0
Towards Benchmarking Explainable Artificial Intelligence Methods—0
Explainable AI for tailored electricity consumption feedback -- an experimental evaluation of visualizations—0
Augmented cross-selling through explainable AI -- a case from energy retailing—0
SoK: Explainable Machine Learning for Computer Security ApplicationsCode0
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