References
Aas, K., Jullum, M., & Løland, A. (2021). Explaining individual predictions when features are dependent: More accurate approximations to Shapley values. Artificial Intelligence, 298, 103502. https://doi.org/10.1016/j.artint.2021.103502 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 Bhatt, U., Xiang, A., Sharma, S., Weller, A., Taly, A., Jia, Y., Ghosh, J., Puri, R., Moura, J. M. F., & Eckersley, P. (2020). Explainable machine learning in deployment. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 648-657). https://doi.org/10.1145/3351095.3375624 Bracke, P., Datta, A., Jung, C., & Sen, S. (2019). Machine learning explainability in finance: An application to default risk analysis. Bank of England Working Paper No. 816. https://doi.org/10.2139/ssrn.3435104 Chen, J., Song, L., Wainwright, M. J., & Jordan, M. I. (2019). L-Shapley and C-Shapley: Efficient model interpretation for structured data. In International Conference on Learning Representations. https://doi.org/10.48550/arXiv.1808.02610 Covert, I., Lundberg, S., & Lee, S. I. (2020). Understanding global feature contributions with additive importance measures. In Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (pp. 17212-17223). https://doi.org/10.48550/arXiv.2004.00668 Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5), 1-42. https://doi.org/10.1145/3236009 Kumar, E., Venkatasubramanian, S., Scheidegger, C., & Friedler, S. (2020). Problems with Shapley-value-based explanations as feature importance measures. In Proceedings of the 37th International Conference on Machine Learning (pp. 5491-5500). https://doi.org/10.48550/arXiv.2002.11097 Kumar, I. E., Venkatasubramanian, S., Scheidegger, C., & Friedler, S. (2021). Shapley residuals: Quantifying the limits of the Shapley value for explanations. In Advances in Neural Information Processing Systems 34 (NeurIPS 2021) (pp. 26598-26608). https://doi.org/10.48550/arXiv.2106.14139 Lauritsen, S. M., Kristensen, M., Olsen, M. V., Larsen, M. S., Lauritsen, K. M., Jørgensen, M. J., Lange, J., & Thiesson, B. (2020). Explainable artificial intelligence model to predict acute critical illness from electronic health records. Nature Communications, 11(1), https://doi.org/10.1038/s41467-020-17431-x Lipton, Z. C. (2018a). The mythos of model interpretability. Queue, 16(3), 31-57. https://doi.org/10.1145/3236386.3241340 Lipton, Z. C. (2018b). The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Communications of the ACM, 61(10), 36-43. https://doi.org/10.1145/3233231 Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S. I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56-67. https://doi.org/10.1038/s42256-019-0138-9 Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30 (NIPS 2017) (pp. 4765-4774). https://doi.org/10.48550/arXiv.1705.07874 Molnar, C. (2022). Interpretable machine learning: A guide for making black box models explainable (2nd ed.). https://christophm.github.io/interpretable-ml-book/ Mothilal, R. K., Sharma, A., & Tan, C. (2020). Explaining machine learning classifiers through diverse counterfactual explanations. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 607-617). https://doi.org/10.1145/3351095.3372850 Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Why should I trust you? Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135-1144). https://doi.org/10.1145/2939672.2939778 Rudin, C. (2019a). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206- https://doi.org/10.1038/s42256-019-0048-x Rudin, C. (2019b). Please stop explaining black box models for high stakes decisions. In Proceedings of the Conference on Neural Information Processing Systems. https://doi.org/10.48550/arXiv.1811.10154 Slack, D., Hilgard, S., Jia, E., Singh, S., & Lakkaraju, H. (2020). Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (pp. 180-186). https://doi.org/10.1145/3375627.3375830 Sundararajan, M., Taly, A., & Yan, Q. (2017). Axiomatic attribution for deep networks. In Proceedings of the 34th International Conference on Machine Learning (pp. 3319- 3328). https://doi.org/10.48550/arXiv.1703.01365 Ustun, B., Spangher, A., & Liu, Y. (2019). Actionable recourse in linear classification. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 10- 19). https://doi.org/10.1145/3287560.3287566 Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a right to explanation of automated decision-making does not exist in the general data protection regulation. International