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Reading list for continued learning

Foundation texts to get going:

  1. Molnar, C. (2020). Interpretable Machine Learning. (https://christophm.github.io/interpretable-ml-book/)
  2. David Gunning et al., XAI—Explainable artificial intelligence. Sci. Robot. 4,eaay7120(2019). (https://doi.org/10.1126/scirobotics.aay7120)

For a refresh on ML & statistics before hitting the explainable frameworks, see:

  • Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. "The elements of statistical learning." (2009).

Other referenced texts

  • Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5):206–215.

  • Miller, Tim. "Explanation in artificial intelligence: Insights from the social sciences." Artificial intelligence 267 (2019): 1-38. (https://www.sciencedirect.com/science/article/pii/S0004370218305988)

  • Barredo-Arrieta, A. B., D´ıaz-Rodr´ıguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garc´ıa, S., Gil-L´opez, S., Molina, D., Benjamins, R., Chatila, R., and Herrara, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58:82–115. (https://www.sciencedirect.com/science/article/pii/S1566253519308103)

  • Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. (https://arxiv.org/pdf/1702.08608)

  • Derks, I. P. and De Waal, A. (2021). A taxonomy of explainable Bayesian networks. In Southern African Conference for Artificial Intelligence Research, pages 220–235. Springer.

  • Freiesleben, Timo. "The intriguing relation between counterfactual explanations and adversarial examples." Minds and Machines 32.1 (2022): 77-109.

  • A. Adadi and M. Berrada, "Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)," in IEEE Access, vol. 6, pp. 52138-52160, 2018, doi: 10.1109/ACCESS.2018.2870052.

  • Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. "" Why should i trust you?" Explaining the predictions of any classifier." Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 2016.