Savo G. Glisic - Artificial Intelligence and Quantum Computing for Advanced Wireless Networks

1 1 R. Caruana, Y. Lou, J. Gehrke, P. Koch, M. Sturm, and N. Elhadad, “Intelligible models for healthcare: Predicting pneumonia risk and hospital 30‐day readmission,” in Proc. 21th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2015, pp. 1721–1730.

2 2 A. Howard, C. Zhang, and E. Horvitz, “Addressing bias in machine learning algorithms: A pilot study on emotion recognition for intelligent systems,” in Proc. Adv. Robot. Social Impacts (ARSO), Mar. 2017, pp. 1–7.

3 3 (2016). European Union General Data Protection Regulation (GDPR). Accessed: Jun. 6, 2018. [Online]. Available: http://www.eugdpr.org

4 4 D. Silver, J. Schrittwieser, K. Simonyan, et al., “Mastering the game of go without human knowledge,” Nature, vol. 550, no. 7676, pp. 354–359, 2017.

5 5 M. Bojarski, D. Del Testa, D. Dworakowski, et al. (2016). “End to end learning for self‐driving cars.” [Online]. Available: https://arxiv.org/abs/1604.07316

6 6 J. Haspiel, J. Meyerson, L.P. Robert Jr, et al. (2018). Explanations and Expectations: Trust Building in Automated Vehicles, http://deepblue.lib.umich.edu. [Online]. Available: https://doi.org/10.1145/3173386.3177057

7 7 A. Holzinger, C. Biemann, C. S. Pattichis, and D. B. Kell. (2017). “What do we need to build explainable AI systems for the medical domain?” [Online]. Available: https://arxiv.org/abs/1712.09923

8 8 G. J. Katuwal and R. Chen. (2016). Machine Learning Model Interpretability for Precision Medicine. [Online]. Available: https://arxiv. org/abs/1610.09045

9 9 Z. Che, S. Purushotham, R. Khemani, and Y. Liu, “Interpretable deep models for ICU outcome prediction,” in Proc. AMIA Annu. Symp., 2017, pp. 371–380

10 10 S. Tan, R. Caruana, G. Hooker, and Y. Lou. (2018). “Detecting bias in black‐box models using transparent model distillation.” [Online]. Available: https://arxiv.org/abs/1710.06169

11 11 C. Howell, “A framework for addressing fairness in consequential machine learning,” in Proc. FAT Conf., Tuts., 2018, pp. 1–2.

12 12 Berk, R. and Bleich, J. (2013). Statistical procedures for forecasting criminal behavior: a comparative assessment. Criminol. Public Policy 12 (3): 513–544.

13 13 Equifax. (2018). Equifax Launches NeuroDecision Technology. Accessed: Jun. 6, 2018. [Online]. Available: https://investor.equifax.comnews‐and‐events/news/2018/03‐26‐2018‐143044126

14 14 D. Gunning. Explainable artificial intelligence (XAI), Defense Advanced Research Projects Agency (DARPA). Accessed: Jun. 6, 2018. [Online]. Available: http://www.darpa.mil/program/explainable‐artificialintelligence

15 15 W. Knight. (2017). The U.S. military wants its autonomous machines to explain themselves, MIT Technology Review. Accessed: Jun. 6, 2018. [Online]. Available: https://www.technologyreview.com/s/603795/theus‐military‐wants‐its‐autonomous‐machines‐to‐explain‐themselves

16 16 A. Henelius, K. Puolamäki, and A. Ukkonen. (2017). “Interpreting classifiers through attribute interactions in datasets.” [Online]. Available: https://arxiv.org/abs/1707.07576

17 17 Future of Privacy Forum. (2017). Unfairness by Algorithm: Distilling the Harms of Automated Decision‐Making. Accessed: Jun. 6, 2018. [Online]. Available: https://fpf.org/wp‐content/uploads/2017/12/FPF‐AutomatedDecision‐Making‐Harms‐and‐Mitigation‐Charts.pdf

18 18 Letham, B., Rudin, C., McCormick, T.H., and Madigan, D. (2015). Interpretable classifiers using rules and Bayesian analysis: building a better stroke prediction model. Ann. Appl. Stat. 9 (3): 1350–1371.

19 19 K. Xu, J. Lei Ba, R. Kiros, et al., “Show, attend and tell: Neural image caption generation with visual attention,” in Proc. Int. Conf. Mach. Learn. (ICML), 2015, pp. 1–10

20 20 Ustun, B. and Rudin, C. (2015). Supersparse linear integer models for optimized medical scoring systems. Mach. Learn. 102 (3): 349–391.

21 21 S. Sarkar, “Accuracy and interpretability trade‐offs in machine learning applied to safer gambling,” in Proc. CEUR Workshop, 2016, pp. 79–87.

22 22 Breiman, L. (2001). Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16 (3): 199–231.

23 23 Z. C. Lipton, “The mythos of model interpretability,” in Proc. ICML Workshop Hum. Interpretability Mach. Learn., 2016, pp. 96–100.

24 24 Krening, S., Harrison, B., Feigh, K.M. et al. (2016). Learning from explanations using sentiment and advice in RL. IEEE Trans. Cogn. Develop. Syst. 9 (1): 44–55.

25 25 A. Mahendran and A. Vedaldi, “Understanding deep image representations by inverting them,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2015, pp. 5188–5196.

26 26 T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” in Proc. Adv. Neural Inf. Process. Syst. (NIPS), 2013, pp. 3111–3119.

27 27 G. Ras, M. van Gerven, and P. Haselager. (2018). “Explanation methods in deep learning: Users, values, concerns and challenges.” [Online]. Available: https://arxiv.org/abs/1803.07517

28 28 A. Santoro, D. Raposo, D.G.T. Barret, et al. (2017). “A simple neural network module for relational reasoning.” [Online]. Available: https://arxiv.org/abs/1706.01427

29 29 R. B. Palm, U. Paquet, and O. Winther. (2017). “Recurrent relational networks for complex relational reasoning.” [Online]. Available: https://arxiv.org/abs/1711.08028

30 30 Y. Dong, H. Su, J. Zhu, and B. Zhang, “Improving interpretability of deep neural networks with semantic information,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Mar. 2017, pp. 4306–4314.

31 31 C. Louizos, U. Shalit, J. M. Mooij, D. Sontag, R. Zemel, and M. Welling, “Causal effect inference with deep latent‐variable models,” in Proc. Adv. Neural Inf. Process. Syst. (NIPS), 2017, pp. 6446–6456.

32 32 O. Goudet, D. Kalainathan, P. Caillou, et al. (2017). “Learning functional causal models with generative neural networks.” [Online]. Available: https://arxiv.org/abs/1709.05321

33 33 C. Yang, A. Rangarajan, and S. Ranka. (2018). “Global model interpretation via recursive partitioning.” [Online]. Available: https://arxiv.org/abs/1802.04253

34 34 M. A. Valenzuela‐Escárcega, A. Nagesh, and M. Surdeanu. (2018). “Lightly‐supervised representation learning with global interpretability.” [Online]. Available: https://arxiv.org/abs/1805.11545

35 35 A. Nguyen, A. Dosovitskiy, J. Yosinski, T. Brox, and J. Clune, “Synthesizing the preferred inputs for neurons in neural networks via deep generator networks,” in Proc. Adv. Neural Inf. Process. Syst. (NIPS), 2016, pp. 3387–3395.

36 36 D. Erhan, A. Courville, and Y. Bengio, “Understanding representations learned in deep architectures,” Dept. d'Informatique Recherche Operationnelle, Univ. Montreal, Montreal, QC, Canada, Tech. Rep. 1355, 2010

37 37 M. T. Ribeiro, S. Singh, and C. Guestrin, “‘Why should I trust you?’ Explaining the predictions of any classifier,” 22nd ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2016, pp. 1135–1144

38 38 M. T. Ribeiro, S. Singh, and C. Guestrin, “Anchors: High‐precision model‐agnostic explanations,” in Proc. AAAI Conf. Artif. Intell., 2018, pp. 1–9.

39 39 J. Lei, M. G'Sell, A. Rinaldo, R. J. Tibshirani, and L. Wasserman, “Distribution‐free predictive inference for regression,” J. Amer. Stat. Assoc., to be published. [Online]. Available: http://www.stat.cmu.edu/~ryantibs/papers/conformal.pdf

40 40 Baehrens, D., Schroeter, T., Harmeling, S. et al. (2010). How to explain individual classification decisions. J. Mach. Learn. Res. 11 (6): 1803–1831.

41 41 K. Simonyan, A. Vedaldi, and A. Zisserman. (2013). “Deep inside convolutional networks: Visualising image classification models and saliency maps.” [Online]. Available: https://arxiv.org/abs/1312.6034