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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
Explainable AI needs formal notions of explanation correctness0
Counterfactual Explanations for Clustering Models0
Additive-feature-attribution methods: a review on explainable artificial intelligence for fluid dynamics and heat transfer0
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 Models0
Deep Learning for predicting rate-induced tipping0
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 Challenges0
Discovering Cyclists' Visual Preferences Through Shared Bike Trajectories and Street View Images Using Inverse Reinforcement LearningCode0
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