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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 181190 of 971 papers

TitleStatusHype
Argumentation-based Agents that Explain their Decisions0
Creating an Explainable Intrusion Detection System Using Self Organizing Maps0
A Data-Driven Exploration of Elevation Cues in HRTFs: An Explainable AI Perspective Across Multiple Datasets0
Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications0
A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME0
ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques0
Comparative Analysis of Hyperspectral Image Reconstruction Using Deep Learning for Agricultural and Biological Applications0
Comparing interpretation methods in mental state decoding analyses with deep learning models0
Comprehensible Artificial Intelligence on Knowledge Graphs: A survey0
A Practical guide on Explainable AI Techniques applied on Biomedical use case applications0
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