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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 581590 of 971 papers

TitleStatusHype
Rule-based Out-Of-Distribution DetectionCode0
iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams0
Helpful, Misleading or Confusing: How Humans Perceive Fundamental Building Blocks of Artificial Intelligence Explanations0
Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate ScienceCode0
Explainable Artificial Intelligence and Cybersecurity: A Systematic Literature Review0
Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision support0
Directive Explanations for Monitoring the Risk of Diabetes Onset: Introducing Directive Data-Centric Explanations and Combinations to Support What-If Explorations0
Using Explainable AI to Cross-Validate Socio-economic Disparities Among Covid-19 Patient Mortality0
Explainable artificial intelligence toward usable and trustworthy computer-aided early diagnosis of multiple sclerosis from Optical Coherence Tomography0
A novel approach to generate datasets with XAI ground truth to evaluate image modelsCode0
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