Interpretable machine learning: Fundamental principles and 10 grand challenges

Author:

Rudin Cynthia,Chen Chaofan,Chen Zhi,Huang Haiyang,Semenova Lesia,Zhong Chudi

Publisher

Institute of Mathematical Statistics

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Reference341 articles.

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2. Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M. and Kim, B. (2018). Sanity Checks for Saliency Maps. In Proceedings of Conference on Neural Information Processing Systems (NeurIPS).

3. Adel, T., Ghahramani, Z. and Weller, A. (2018). Discovering interpretable representations for both deep generative and discriminative models. In Proceedings of International Conference on Machine Learning (ICML) 50–59.

4. Afnan, M. A. M., Rudin, C., Conitzer, V., Savulescu, J., Mishra, A., Liu, Y. and Afnan, M. (2021). Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization. In Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES).

5. Agarwal, R., Frosst, N., Zhang, X., Caruana, R. and Hinton, G. E. (2020). Neural additive models: Interpretable machine learning with neural nets. In Proceedings of the ICML Workshop on Human Interpretability in Machine Learning.

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