Top

Hint

Swipe to navigate through the chapters of this book

Published in:

Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Machine Learning – The Results Are Not the only Thing that Matters! What About Security, Explainability and Fairness?

Authors : Michał Choraś, Marek Pawlicki, Damian Puchalski, Rafał Kozik

Published in: Computational Science – ICCS 2020

Publisher: Springer International Publishing

Abstract

Recent advances in machine learning (ML) and the surge in computational power have opened the way to the proliferation of ML and Artificial Intelligence (AI) in many domains and applications. Still, apart from achieving good accuracy and results, there are many challenges that need to be discussed in order to effectively apply ML algorithms in critical applications for the good of societies. The aspects that can hinder practical and trustful ML and AI are: lack of security of ML algorithms as well as lack of fairness and explainability. In this paper we discuss those aspects and provide current state of the art analysis of the relevant works in the mentioned domains.

To get access to this content you need the following product:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 69.000 Bücher
über 500 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt 90 Tage mit der neuen Mini-Lizenz testen!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 50.000 Bücher
über 380 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt 90 Tage mit der neuen Mini-Lizenz testen!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 58.000 Bücher
über 300 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt 90 Tage mit der neuen Mini-Lizenz testen!

inform now

previous chapter Experiencer Detection and Automated Extraction of a Family Disease Tree from Medical Texts in Russian Language

next chapter Syntactic and Semantic Bias Detection and Countermeasures

Choraś, M., Pawlicki, M., Kozik, R.: The feasibility of deep learning use for adversarial model extraction in the cybersecurity domain. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A.J., Menezes, R., Allmendinger, R. (eds.) IDEAL 2019. LNCS, vol. 11872, pp. 353–360. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33617-2_36 CrossRef

Chakraborty, A., Alam, M., Dey, V., Chattopadhyay, A., Mukhopadhyay, D.: Adversarial attacks and defences: a survey. arXiv preprint arXiv:1810.00069 (2018)

Liao, X., Ding, L., Wang, Y.: Secure machine learning, a brief overview. In: 2011 Fifth International Conference on Secure Software Integration and Reliability Improvement-Companion, pp. 26–29. IEEE (2011)

Papernot, N., McDaniel, P., Sinha, A., Wellman, M.P.: SoK: security and privacy in machine learning. In: 2018 IEEE European Symposium on Security and Privacy (EuroS&P), pp. 399–414. IEEE (2018)

Biggio, B., Nelson, B., Laskov, P.: Poisoning attacks against support vector machines. arXiv preprint arXiv:1206.6389 (2012)

Nelson, B., et al.: Exploiting machine learning to subvert your spam filter. LEET 8, 1–9 (2008)

Xiao, H., Biggio, B., Brown, G., Fumera, G., Eckert, C., Roli, F.: Is feature selection secure against training data poisoning? In: International Conference on Machine Learning, pp. 1689–1698 (2015)

Biggio, B., Fumera, G., Roli, F.: Pattern recognition systems under attack: design issues and research challenges. Int. J. Pattern Recognit. Artif. Intell. 28(07), 1460002 (2014) CrossRef

Shafahi, A., et al.: Poison frogs! Targeted clean-label poisoning attacks on neural networks. In: Advances in Neural Information Processing Systems, pp. 6103–6113 (2018)

10.

Muñoz-González, L., et al.: Towards poisoning of deep learning algorithms with back-gradient optimization. In: Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security - AISec 17. ACM Press (2017)

11.

Papernot, N., McDaniel, P., Goodfellow, I.: Transferability in machine learning: from phenomena to black-box attacks using adversarial samples. arXiv preprint arXiv:1605.07277 (2016)

12.

Demontis, A., et al.: Why do adversarial attacks transfer? Explaining transferability of evasion and poisoning attacks. In: 28th \(\{\)USENIX \(\}\) Security Symposium ( \(\{\)USENIX \(\}\) Security 19), pp. 321–338 (2019)

13.

Mei, S., Zhu, X.: Using machine teaching to identify optimal training-set attacks on machine learners. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)

14.

Yang, C., Wu, Q., Li, H., Chen, Y.: Generative poisoning attack method against neural networks. arXiv preprint arXiv:1703.01340 (2017)

15.

Chen, X., Liu, C., Li, B., Lu, K., Song, D.: Targeted backdoor attacks on deep learning systems using data poisoning. arXiv preprint arXiv:1712.05526 (2017)

16.

Zhong, Z.: A tutorial on fairness in machine learning, July 2019

17.

Barocas, S., Selbst, A.D.: Big data’s disparate impact. SSRN Electron. J. 104, 671 (2016)

18.

Gajane,P., Pechenizkiy, M.: On Formalizing Fairness in Prediction with Machine Learning. arXiv e-prints, page arXiv:1710.03184, October 2017

19.

Verma, S., Rubin, J.: Fairness definitions explained. In: Proceedings of the International Workshop on Software Fairness - FairWare 18. ACM Press (2018)

20.

Hardt, M., Price, E., Srebro, N., et al.: Equality of opportunity in supervised learning. In: Advances in Neural Information Processing Systems, pp. 3315–3323 (2016)

21.

Zafar, M.B., Valera, I., Rodriguez, M.G., Gummadi, K.P.: Fairness beyond disparate treatment & disparate impact. In: Proceedings of the 26th International Conference on World Wide Web - WWW 17. ACM Press (2017)

22.

Del Barrio, E., Gamboa, F., Gordaliza, P., Loubes, J.-M.: Obtaining fairness using optimal transport theory. arXiv preprint arXiv:1806.03195 (2018)

23.

Zemel, R., Wu, Y., Swersky, K., Pitassi, T., Dwork, C.: Learning fair representations. In: International Conference on Machine Learning, pp. 325–333 (2013)

24.

Louizos, C., Swersky, K., Li, Y., Welling, M., Zemel, R.: The variational fair autoencoder. arXiv preprint arXiv:1511.00830 (2015)

25.

Lum, K., Johndrow, J.: A statistical framework for fair predictive algorithms. arXiv preprint arXiv:1610.08077 (2016)

26.

Calmon, F., Wei, D., Vinzamuri, B., Ramamurthy, K.N., Varshney, K.R.: Optimized pre-processing for discrimination prevention. In: Advances in Neural Information Processing Systems, pp. 3992–4001 (2017)

27.

Iosifidis, V., Ntoutsi, E.: Dealing with bias via data augmentation in supervised learning scenarios. Jo Bates Paul D. Clough Robert Jäschke, p. 24 (2018)

28.

Sharma, S., Zhang, Y., Aliaga, J.M.R., Bouneffouf, D., Muthusamy, V., Varshney, K.R.: Data augmentation for discrimination prevention and bias disambiguation. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 358–364 (2020)

29.

Woodworth, B., Gunasekar, S., Ohannessian, M.I., Srebro,N.: Learning non-discriminatory predictors. arXiv preprint arXiv:1702.06081 (2017)

30.

Zafar, M.B., Valera, I., Rodriguez, M.G., Gummadi, K.P.: Fairness constraints: mechanisms for fair classification. arXiv preprint arXiv:1507.05259 (2015)

31.

Agarwal, A., Beygelzimer, A., Dudík, M., Langford, J., Wallach, H.: A reductions approach to fair classification. arXiv preprint arXiv:1803.02453 (2018)

32.

Gall, R.: Machine learning explainability vs interpretability: two concepts that could help restore trust in AI. KDnuggets News 19(1) (2019)

33.

Dosilovic, F.K., Brcic, M., Hlupic, N.: Explainable artificial intelligence: a survey. In 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). IEEE, May 2018

34.

Bhatt, U., et al.: Explainable machine learning in deployment. arXiv preprint arXiv:1909.06342 (2019)

35.

Albert, C.: We are ready for machine learning explainability?, June 2019. https://towardsdatascience.com/we-are-ready-to-ml-explainability-2e7960cb950d. Accessed 31 Mar 2020

36.

Etmann, C., Lunz, S., Maass, P., Schönlieb, C.-B.: On the connection between adversarial robustness and saliency map interpretability. arXiv preprint arXiv:1905.04172 (2019)

37.

Koh, P.W., Liang, P.: Understanding black-box predictions via influence functions. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 1885–1894. JMLR.org (2017)

38.

Carvalho, D.V., Pereira, E.M., Cardoso, J.S.: Machine learning interpretability: a survey on methods and metrics. Electronics 8(8), 832 (2019) CrossRef

39.

Molnar, C.: A guide for making black box models explainable (2018). https://christophm.github.io/interpretable-ml-book/. Accessed 28 Mar 2019

40.

Robnik-Šikonja, M., Bohanec, M.: Perturbation-based explanations of prediction models. In: Zhou, J., Chen, F. (eds.) Human and Machine Learning. HIS, pp. 159–175. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-90403-0_9 CrossRef

41.

Silva, W., Fernandes, K., Cardoso, M.J., Cardoso, J.S.: Towards complementary explanations using deep neural networks. In: Stoyanov, D., et al. (eds.) MLCN/DLF/IMIMIC -2018. LNCS, vol. 11038, pp. 133–140. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02628-8_15 CrossRef

42.

Honegger, M.: Shedding light on black box machine learning algorithms: development of an axiomatic framework to assess the quality of methods that explain individual predictions. arXiv preprint arXiv:1808.05054 (2018)

Title: Machine Learning – The Results Are Not the only Thing that Matters! What About Security, Explainability and Fairness?
Authors: Michał Choraś
Marek Pawlicki
Damian Puchalski
Rafał Kozik
Publisher: Springer International Publishing
Book: Computational Science – ICCS 2020
Print ISBN: 978-3-030-50422-9

Electronic ISBN: 978-3-030-50423-6

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-50423-6_46

Springer Professional

Hint

Swipe to navigate through the chapters of this book

Abstract

Please log in to get access to this content

To get access to this content you need the following product:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"