Comments on: Data science, big data and statistics
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The paper under discussion offers an accurate panoramic view about the effects that the emergence of big data and data science in the last decade is having on statistics. I congratulate the authors for such a well-written and stimulating piece of work, including an excellent bibliography review. I agree with them that statistics has been able to adapt to the new scenarios (abundance and heterogeneity of data, impressive computing capacity, other disciplines sharing the same objectives, among other), and I trust that the discipline will take advantage of the new challenges it will face in the future.
The authors’ belief on the need for convergence of different disciplines (statistics, machine learning, operation research, mathematics) is a position that I share completely. Specifically, they foresee that statistical ideas will be used to decompose and understand the forecasting rules created in other areas, [and] to identify the importance of the more relevant variables....
Work supported in part by the Spanish Ministerio de Economía, Industria y Competitividad, Grant MTM2017-88142-P.
- Biecek P (2018) DALEX: explainers for complex predictive models in R. arXiv:1806.08915v2
- Biran O, Cotton C (2017) Explanation and justification in machine learning: a survey. In: Proceedings of The IJCAI-17 workshop on Explainable AI (XAI), pp 8–13Google Scholar
- Datta A, Sen S, Zick Y (2016) Algorithmic transparency via quantitative input influence: theory and experiments with learning systems. In: 2016 IEEE symposium on security and privacy (SP). IEEE, pp 598–617Google Scholar
- Ferrando P (2018) Lighting the black box: explaining individual predictions of machine learning algorithms. Master Thesis, MESIO UPC-UB. Advisors: Belanche, L. and Delicado, P. http://hdl.handle.net/2117/113463. Accessed 13 Dec 2018
- Fisher A, Rudin C, Dominici F (2018) All models are wrong but many are useful: Variable importance for black-box, proprietary, or misspecified prediction models, using model class reliance. arXiv:1801.01489v3
- Nott G (2017) Explainable artificial intelligence: cracking open the black box of AI. Computer World. https://www.computerworld.com.au/article/617359. Accessed 12 Dec 2018
- Ribeiro MT, 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. ACM, pp 1135–1144Google Scholar
- Staniak M, Biecek P (2018) Explanations of model predictions with live and breakDown packages. arXiv:1804.01955
- Wikipedia Contributors: Explainable Artificial Intelligence (2018) Wikipedia, the free encyclopedia (2018). https://en.wikipedia.org/wiki/Explainable_Artificial_Intelligence. Accessed 12 Dec 2018