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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 571580 of 971 papers

TitleStatusHype
Shapley-based Explainable AI for Clustering Applications in Fault Diagnosis and PrognosisCode0
Rough Randomness and its Application0
Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma0
cito: An R package for training neural networks using torchCode0
Contextual Trust0
EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust ModelsCode0
Challenges facing the explainability of age prediction models: case study for two modalitiesCode0
Explainable AI for Time Series via Virtual Inspection Layers0
Analysis and Evaluation of Explainable Artificial Intelligence on Suicide Risk Assessment0
A Survey on Explainable Artificial Intelligence for Cybersecurity0
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