A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods-Reference-Cited by-同舟云学术

A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods

Published:2022-06-20 Issue: Volume: Page:
ISSN:
Container-title:2022 ACM Conference on Fairness, Accountability, and Transparency
language:
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Author:

Speith Timo¹

Affiliation:

1. Institute of Philosophy and Department of Computer Science, Saarland University, Germany

Funder

Volkswagen Foundation

Deutsche Forschungsgemeinschaft

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3531146.3534639

Reference79 articles.

1. Trends and Trajectories for Explainable, Accountable and Intelligible Systems

2. Julius Adebayo Justin Gilmer Michael Muelly Ian Goodfellow Moritz Hardt and Been Kim. 2020. Sanity Checks for Saliency Maps. arXiv:1810.03292 Julius Adebayo Justin Gilmer Michael Muelly Ian Goodfellow Moritz Hardt and Been Kim. 2020. Sanity Checks for Saliency Maps. arXiv:1810.03292

3. Using arguments for making and explaining decisions

4. To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods

5. Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability

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