Shapley-Lorenz eXplainable Artificial Intelligence

Author:

Giudici Paolo,Raffinetti Emanuela

Funder

European Commission

Horizon 2020

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference20 articles.

1. Aas, K., Jullum, M., & Loland, A. (2020). Explaining individual predictions when features are dependent: More accurate approximations to Shapley values. arXiv preprint arXiv:1903.10464.

2. “What is relevant in a text document?”: An interpretable machine learning approach;Arras;PLoS One,2017

3. Arrieta, A. B., Dríaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2019). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. arXiv preprint arXiv:1910.10045.

4. Bracke, P., Datta, A., Jung, C., & Shayak, S. (2019). Machine learning explainability in finance: An application to default risk analysis. Staff Working Paper No. 816, Bank of England.

5. Explainable AI in credit risk management;Bussmann;Frontiers in Artificial Intelligence,2020

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