Workshop on Human-Interpretable AI-Reference-Cited by-同舟云学术

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Workshop on Human-Interpretable AI

Published:2024-08-24 Issue: Volume: Page:6708-6709
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Ciravegna Gabriele¹^ORCID,Espinosa Zarlenga Mateo²^ORCID,Barbiero Pietro³^ORCID,Giannini Francesco⁴^ORCID,Shams Zohreh²^ORCID,Garreau Damien⁵^ORCID,Jamnik Mateja²^ORCID,Cerquitelli Tania¹^ORCID

Affiliation:

1. Dipartimento di Automatica e Informatica, Politecnico di Torino, Torino, Italy

2. University of Cambridge, Cambridge, United Kingdom

3. Università della Svizzera Italiana, Lugano, Swiss

4. Scuola Normale Superiore, Pisa, Italy

5. Julius-Maximilians-Universität Würzburg, Würzburg, Germany

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671499

Reference11 articles.

1. Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim. 2018. Sanity checks for saliency maps. Advances in neural information processing systems, Vol. 31 (2018).

2. David Alvarez Melis and Tommi Jaakkola. 2018. Towards robust interpretability with self-explaining neural networks. Advances in neural information processing systems, Vol. 31 (2018).

3. Logic Explained Networks

4. Bryce Goodman and Seth Flaxman. 2017. European Union regulations on algorithmic decision-making and a ?right to explanation". AI magazine, Vol. 38, 3 (2017), 50--57.

5. Extending Logic Explained Networks to Text Classification

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