Review of measures for improving ML model interpretability: Empowering decision makers with transparent insights-Reference-Cited by-同舟云学术

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Review of measures for improving ML model interpretability: Empowering decision makers with transparent insights

Published:2024-02-01 Issue: Volume:101 Page:1-5
ISSN:
Container-title:2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)
language:
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Author:

Arsic Sinisa¹,Mihic Marko²,Petrovic Dejan²,Mitrovic Zorica²,Kostic Slavica Cicvaric³,Mihic Olivera⁴

Affiliation:

1. Telekom Serbia,Intelligent Automation, Digital and Strategy Division,Belgrade,Serbia

2. University of Belgrade,Faculty of Organizational Sciences,Department for Management and Project Management,Belgrade,Serbia

3. University of Belgrade,Faculty of Organizational Sciences,Department for Marketing Management and PR,Belgrade,Serbia

4. University of Belgrade,Faculty of Organizational Sciences,Belgrade,Serbia

Funder

Science Fund of the Republic of Serbia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10467177/10467211/10467907.pdf?arnumber=10467907

Reference29 articles.

1. Quantitative management in the function of management support;Arsic

2. Explainable Artificial Intelligence for Sarcasm Detection in Dialogues

3. "Why Should I Trust You?"

4. From local explanations to global understanding with explainable AI for trees

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