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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 151–175 of 971 papers

TitleStatusHype
Explainable Earth Surface Forecasting under Extreme EventsCode1
Easydiagnos: a framework for accurate feature selection for automatic diagnosis in smart healthcare—0
Tackling the Accuracy-Interpretability Trade-off in a Hierarchy of Machine Learning Models for the Prediction of Extreme HeatwavesCode0
Developing Guidelines for Functionally-Grounded Evaluation of Explainable Artificial Intelligence using Tabular Data—0
Leveraging CAM Algorithms for Explaining Medical Semantic SegmentationCode0
Examining the Rat in the Tunnel: Interpretable Multi-Label Classification of Tor-based Malware—0
Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution—0
Statistical tuning of artificial neural network—0
Deep Learning for Precision Agriculture: Post-Spraying Evaluation and Deposition EstimationCode0
From Pixels to Words: Leveraging Explainability in Face Recognition through Interactive Natural Language Processing—0
Explainable AI needs formal notions of explanation correctness—0
Counterfactual Explanations for Clustering Models—0
Additive-feature-attribution methods: a review on explainable artificial intelligence for fluid dynamics and heat transfer—0
Cartan moving frames and the data manifoldsCode0
Harnessing AI data-driven global weather models for climate attribution: An analysis of the 2017 Oroville Dam extreme atmospheric riverCode0
Global Lightning-Ignited Wildfires Prediction and Climate Change Projections based on Explainable Machine Learning Models—0
Deep Learning for predicting rate-induced tipping—0
Confident Teacher, Confident Student? A Novel User Study Design for Investigating the Didactic Potential of Explanations and their Impact on UncertaintyCode1
Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges—0
Discovering Cyclists' Visual Preferences Through Shared Bike Trajectories and Street View Images Using Inverse Reinforcement LearningCode0
Stacked ensemble\-based mutagenicity prediction model using multiple modalities with graph attention network—0
Interpreting Outliers in Time Series Data through Decoding Autoencoder—0
Breaking Down Financial News Impact: A Novel AI Approach with Geometric Hypergraphs—0
Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction—0
Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between Features—0
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