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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 111120 of 971 papers

TitleStatusHype
A Deep Dive into Perturbations as Evaluation Technique for Time Series XAICode0
Evaluating saliency methods on artificial data with different background typesCode0
An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain InjuryCode0
Explainable Artificial Intelligence for Dependent Features: Additive Effects of CollinearityCode0
Explainability of Machine Learning Models under Missing DataCode0
Improving Deep Neural Network Classification Confidence using Heatmap-based eXplainable AICode0
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AICode0
Explainable Authorship Identification in Cultural Heritage Applications: Analysis of a New PerspectiveCode0
Explainable Anomaly Detection for Industrial Control System CybersecurityCode0
Enhancing Cluster Analysis With Explainable AI and Multidimensional Cluster PrototypesCode0
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