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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 621630 of 971 papers

TitleStatusHype
Identifying Dominant Industrial Sectors in Market States of the S&P 500 Financial Data0
Towards Benchmarking Explainable Artificial Intelligence Methods0
Explainable AI for tailored electricity consumption feedback -- an experimental evaluation of visualizations0
Augmented cross-selling through explainable AI -- a case from energy retailing0
SoK: Explainable Machine Learning for Computer Security ApplicationsCode0
Shapelet-Based Counterfactual Explanations for Multivariate Time SeriesCode0
ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image RecognitionCode1
Carefully choose the baseline: Lessons learned from applying XAI attribution methods for regression tasks in geoscience0
Causality-Inspired Taxonomy for Explainable Artificial Intelligence0
Transcending XAI Algorithm Boundaries through End-User-Inspired Design0
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