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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 311320 of 971 papers

TitleStatusHype
EXACT: Towards a platform for empirically benchmarking Machine Learning model explanation methods0
Examining the Rat in the Tunnel: Interpretable Multi-Label Classification of Tor-based Malware0
Example-Based Explainable AI and its Application for Remote Sensing Image Classification0
Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches0
Explainability for identification of vulnerable groups in machine learning models0
Explainability in Deep Reinforcement Learning0
Explainability in Deep Reinforcement Learning, a Review into Current Methods and Applications0
Carefully choose the baseline: Lessons learned from applying XAI attribution methods for regression tasks in geoscience0
Applying XAI based unsupervised knowledge discovering for Operation modes in a WWTP. A real case: AQUAVALL WWTP0
Enhancing Counterfactual Image Generation Using Mahalanobis Distance with Distribution Preferences in Feature Space0
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