How to Interpret Machine Knowledge

Fashen Li; Lian Li; Jianping Yin; Yong Zhang; Qingguo Zhou; Kun Kuang

doi:10.1016/j.eng.2019.11.013

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Engineering ›› 2020, Vol. 6 ›› Issue (3) : 218-220. DOI: 10.1016/j.eng.2019.11.013

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How to Interpret Machine Knowledge

Fashen Li^a^,^* ,
Lian Li^b^,^* ,
Jianping Yin^c^,^* ,
Yong Zhang^d^,^* ,
Qingguo Zhou^e^,^* ,
Kun Kuang^f^,^*

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Fashen Li, Lian Li, Jianping Yin, Yong Zhang, Qingguo Zhou, Kun Kuang. How to Interpret Machine Knowledge. Engineering, 2020, 6(3): 218‒220 https://doi.org/10.1016/j.eng.2019.11.013

References

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[1]	Pan Y. Special issue on artificial intelligence 2.0. Front Inf Technol Electron Eng 2017;18(1):1–2.

[2]	Pan Y. 2018 special issue on artificial intelligence 2.0: theories and applications. Front Inf Technol Electron Eng 2018;19(1):1–2.

[3]	Judea Pearl on his inspiration and the breakthrough moments of his research [Internet]. Cambridge: Cambridge University Press; 2012 [cited 2020 Jan 03]. Available from: http://www.cambridgeblog.org/2012/07/qa-with-judea-pearlpart-one/.

[4]	Pearl J, Mackenzie D. The book of why: the new science of cause and effect. New York: Basic Books; 2018.

[5]	Newton I. Mathematical principles of natural philosophy. 2nd ed. London: A. Strahan; 1802.

[6]	Imbens GW, Rubin DB. Causal inference for statistics, social, and biomedical sciences: an introduction. New York: Cambridge University Press; 2015.

[7]	Lucci S, Kopec D. Artificial intelligence in the 21st century. Sterling: Stylus Publishing, LLC; 2015.

[8]	Molnar C. Interpretable machine learning: a guide for making black box models explainable [Internet]. 2019. Available from: https://christophm.github.io/ interpretable-ml-book/.

[9]

Ribeiro MT, Singh S, Guestrin C. ‘‘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; 2016 Aug 13–17; San Francisco, CA, USA. New York: Association for Computing Machinery; 2016. p. 1135–44.