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Interpretable Machine Learning: Explainability in Algorithm Design

Published in Journal of Industrial Engineering and Applied Science • Nov 30, 2024
NobleIDNI8P67W98R69S39
Authors:
Xiaoyan Cheng
,
Chang Che

Abstract

In recent years, there is a high demand for transparency and accountability in machine learning models, especially in domains such as healthcare, finance and etc. In this paper, we delve into deep how to make machine learning models more interpretable, with focus on the importance of the explainabil...

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