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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 871880 of 971 papers

TitleStatusHype
Explainable Authorship Identification in Cultural Heritage Applications: Analysis of a New PerspectiveCode0
Quantifying Explainability of Saliency Methods in Deep Neural Networks with a Synthetic DatasetCode0
Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG SignalsCode0
Explainable Debugger for Black-box Machine Learning ModelsCode0
Introducing User Feedback-based Counterfactual Explanations (UFCE)Code0
Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscienceCode0
Enhancing Cluster Analysis With Explainable AI and Multidimensional Cluster PrototypesCode0
Invisible Users: Uncovering End-Users' Requirements for Explainable AI via Explanation Forms and GoalsCode0
Energy-based Model for Accurate Shapley Value Estimation in Interpretable Deep Learning Predictive ModelingCode0
Explainable expected goal models for performance analysis in football analyticsCode0
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