Explainable Deep Learning: A Visual Analytics Approach with Transition Matrices

Explainable Deep Learning: A Visual Analytics Approach with Transition Matrices

The non-transparency of artificial intelligence (AI) systems, particularly in deep learning (DL), poses significant challenges to their comprehensibility and trustworthiness. This study aims to enhance the explainability of DL models through visual analytics (VA) and human-in-the-loop (HITL) princip...

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Bibliographic Details
Main Authors: Pavlo Radiuk, Olexander Barmak, Eduard Manziuk, Iurii Krak
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:Mathematics
Subjects:
explainable artificial intelligence (XAI)
deep learning
machine learning
visual analytics
human-in-the-loop
model explainability
Online Access:https://www.mdpi.com/2227-7390/12/7/1024

Internet

https://www.mdpi.com/2227-7390/12/7/1024

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