Improving understandability of feature contributions in model-agnostic explainable AI tools-Reference-Cited by-同舟云学术

Improving understandability of feature contributions in model-agnostic explainable AI tools

Published:2022-04-29 Issue: Volume: Page:
ISSN:
Container-title:CHI Conference on Human Factors in Computing Systems
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Author:

Hadash Sophia¹,Willemsen Martijn C.¹,Snijders Chris²,IJsselsteijn Wijnand A.²

Affiliation:

1. Jheronimus Academy of Data Science, Netherlands and Human-Technology Interaction, Eindhoven University of Technology, Netherlands

2. Human Technology Interaction, Eindhoven University of Technology, Netherlands

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3491102.3517650

Reference33 articles.

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3. Vaishak Belle and Ioannis Papantonis . 2020. Principles and practice of explainable machine learning. arXiv ( 2020 ). arxiv:2009.11698 Vaishak Belle and Ioannis Papantonis. 2020. Principles and practice of explainable machine learning. arXiv (2020). arxiv:2009.11698

4. Adrian Brasoveanu , Megan Moodie , and Rakshit Agrawal . 2020 . Textual evidence for the perfunctoriness of independent medical reviews . CEUR Workshop Proceedings 2657 (2020), 1–9. https://doi.org/10.1145/nnnnnnn.nnnnnnn Adrian Brasoveanu, Megan Moodie, and Rakshit Agrawal. 2020. Textual evidence for the perfunctoriness of independent medical reviews. CEUR Workshop Proceedings 2657 (2020), 1–9. https://doi.org/10.1145/nnnnnnn.nnnnnnn

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