A peek inside two black boxes-an experiment with explainable artificial intelligence and IPCC leadership

Author:

Blanke Tobias,Venturini Tommaso,De Pryck Kari

Funder

EC H2020

Publisher

Springer Science and Business Media LLC

Subject

General Medicine

Reference25 articles.

1. Adadi, A., & Berrada, M. (2018). Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052

2. Angelov, P. P., et al. (2021). Explainable artificial intelligence: An analytical review. Wires Data Mining and Knowledge Discovery, 11(5), e1424. https://doi.org/10.1002/widm.1424

3. Baldi, P. (2012) Autoencoders, unsupervised learning, and deep architectures. In Proceedings of ICML Workshop on Unsupervised and Transfer Learning. Proceedings of ICML Workshop on Unsupervised and Transfer Learning, JMLR Workshop and Conference Proceedings (pp. 37–49). ICML. Available at: https://proceedings.mlr.press/v27/baldi12a.html. Accessed 17 June 2022.

4. Blanke, T. (2018). Predicting the past. DHQ: Digital Humanities Quarterly, 12(2). Available at https://www.digitalhumanities.org/dhq/vol/12/2/000377/000377.html. Accessed 17 June 2022.

5. Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 2053951715622512. https://doi.org/10.1177/2053951715622512

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1. Reproducibility and explainability in digital humanities;International Journal of Digital Humanities;2024-01-03

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