Developing a Catalogue of Explainability Methods to Support Expert and Non-expert Users

Author:

Martin KyleORCID,Liret AnneORCID,Wiratunga NirmalieORCID,Owusu Gilbert,Kern Mathias

Publisher

Springer International Publishing

Reference20 articles.

1. Arras, L., Horn, F., Montavon, G., Müller, K.R., Samek, W.: “What is relevant in a text document?”: an interpretable machine learning approach. PLoS ONE 12(8), 1–23 (2017). https://doi.org/10.1371/journal.pone.0181142

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3. Collins, E., Augenstein, I., Riedel, S.: A supervised approach to extractive summarisation of scientific papers. arXiv preprint arXiv:1706.03946 (2017)

4. European Parliament and Council: Regulation (EU) 2016/679 of the European parliament and of the council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46/EC (general data protection regulation)

5. Gunning, D.: Explainable artificial intelligence (XAI). Defense Advanced Research Projects Agency (DARPA), and Web (2017)

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