1. Simon, Herbert A. “Artificial intelligence: an empirical science.” Artificial Intelligence 77.1 (1995): 95-127.

TOP XAI Paper

  1. Miller, Tim. “Explanation in artificial intelligence: Insights from the social sciences.” Artificial Intelligence (2018).
  2. Guidotti, Riccardo, et al. “A survey of methods for explaining black box models.” ACM computing surveys (CSUR) 51.5 (2019): 93.
  3. W Samek, G Montavon, A Vedaldi, LK Hansen, KR Müller (Eds.) Explainable AI: Interpreting, Explaining and Visualizing Deep. Learning Springer LNCS 11700, 2019
  4. Doshi-Velez, Finale, and Been Kim. “Towards a rigorous science of interpretable machine learning.” arXiv preprint arXiv:1702.08608 (2017).
  5. Doshi-Velez, Finale, et al. “Accountability of AI under the law: The role of explanation.” arXiv preprint arXiv:1711.01134 (2017).
  6. Miller, Tim. “But why?: understanding explainable artificial intelligence.” XRDS: Crossroads, The ACM Magazine for Students 25.3 (2019): 20-25.

TOP AI Books

  • 统计学习方法; 李航
  • 统计学习方法（第2版）; 李航
  • 深度学习; Ian Goodfellow, Yoshua Bengio; Aaron Courville
  • Pattern Recognition and Machine Learning; Christopher Bishop
  • 终极算法; Pedro Domingos
  • 为什么; Judea Pearl, Dana Mackenzie