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Volumn 5, Issue 3, 2017, Pages 246-255

On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products

Author keywords

cyber physical systems; data products; decision science; machine learning; safety

Indexed keywords

ALGORITHM; DECISION MAKING; ECONOMICS; MACHINE LEARNING; SAFETY;

EID: 85049006775     PISSN: 21676461     EISSN: 2167647X     Source Type: Journal    
DOI: 10.1089/big.2016.0051     Document Type: Article
Times cited : (185)

References (54)
  • 4
    • 39749101321 scopus 로고    scopus 로고
    • Principles of engineering safety: Risk and uncertainty reduction
    • Möller N, Hansson SO. Principles of engineering safety: Risk and uncertainty reduction. Reliab Eng Syst Safe. 2008;93:798-805.
    • (2008) Reliab Eng Syst Safe , vol.93 , pp. 798-805
    • Möller, N.1    Hansson, S.O.2
  • 5
    • 84911994408 scopus 로고    scopus 로고
    • The concepts of risk and safety
    • In: Roeser S, Hillerbrand R, Sandin P, Peterson M (Eds.), Dordrecht, Netherlands: Springer
    • Möller N. The concepts of risk and safety. In: Roeser S, Hillerbrand R, Sandin P, Peterson M (Eds.): Handbook of Risk Theory, Dordrecht, Netherlands: Springer, 2012. pp. 55-85.
    • (2012) Handbook of Risk Theory , pp. 55-85
    • Möller, N.1
  • 6
    • 84886092148 scopus 로고    scopus 로고
    • Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty
    • Senge R, Bösner S, Dembczynski K, et al. Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty. Inf Sci. 2014;255:16-29.
    • (2014) Inf Sci , vol.255 , pp. 16-29
    • Senge, R.1    Bösner, S.2    Dembczynski, K.3
  • 7
    • 0040864988 scopus 로고
    • Principles of risk minimization for learning theory
    • Vapnik V. Principles of risk minimization for learning theory. Adv Neur Inf Process Syst. 1992;4:831-838.
    • (1992) Adv Neur Inf Process Syst , vol.4 , pp. 831-838
    • Vapnik, V.1
  • 12
    • 84978955059 scopus 로고    scopus 로고
    • Adverse events in robotic surgery: A retrospective study of 14 years of FDA data
    • Alemzadeh H, Raman J, Leveson N, et al. Adverse events in robotic surgery: A retrospective study of 14 years of FDA data. PLoS One. 2016;11:1-20.
    • (2016) PLoS One , vol.11 , pp. 1-20
    • Alemzadeh, H.1    Raman, J.2    Leveson, N.3
  • 14
    • 0037527188 scopus 로고    scopus 로고
    • Improving predictive inference under covariate shift by weighting the log-likelihood function
    • Shimodaira H. Improving predictive inference under covariate shift by weighting the log-likelihood function. J Stat Plan Inference. 2000;90:227-244.
    • (2000) J Stat Plan Inference , vol.90 , pp. 227-244
    • Shimodaira, H.1
  • 15
    • 33749028480 scopus 로고    scopus 로고
    • Domain adaptation for statistical classifiers
    • Daume H III, Marcu D. Domain adaptation for statistical classifiers. J Artif Intell Res 2006;26:101-126.
    • (2006) J Artif Intell Res , vol.26 , pp. 101-126
    • Daume, H.1    Marcu, D.2
  • 16
    • 84954180053 scopus 로고    scopus 로고
    • Elhadad, intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
    • Sydney, Australia
    • Caruana R, Lou Y, Gehrke J, et al. Elhadad, intelligible models for healthcare: predicting pneumonia risk and hospital 30-day readmission. In: Proceedings on ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Sydney, Australia, 2015, pp. 1721-1730.
    • (2015) Proceedings on ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pp. 1721-1730
    • Caruana, R.1    Lou, Y.2    Gehrke, J.3
  • 17
    • 84906334449 scopus 로고    scopus 로고
    • Comprehensible classification models-A position paper
    • Freitas AA. Comprehensible classification models-A position paper. SIGKDD Explorations 2013;15:1-10.
    • (2013) SIGKDD Explorations , vol.15 , pp. 1-10
    • Freitas, A.A.1
  • 21
    • 85018289194 scopus 로고    scopus 로고
    • (accessed September 8, 2017)
    • Wang F, Rudin C. 2015. Causal falling rule lists. Available online at http:// arxiv. org/pdf/1510. 05189. pdf (accessed September 8, 2017).
    • (2015) Causal Falling Rule Lists
    • Wang, F.1    Rudin, C.2
  • 24
    • 0035283313 scopus 로고    scopus 로고
    • Robust classification for imprecise environments
    • Provost F, Fawcett T. Robust classification for imprecise environments. Mach Learn. 2001;42:203-231.
    • (2001) Mach Learn , vol.42 , pp. 203-231
    • Provost, F.1    Fawcett, T.2
  • 26
    • 84878282973 scopus 로고    scopus 로고
    • A methodology for direct and indirect discrimination prevention in data mining
    • Hajian S, Domingo-Ferrer J. A methodology for direct and indirect discrimination prevention in data mining. IEEE Trans Knowl Data Eng. 2013;25:1445-1459.
    • (2013) IEEE Trans Knowl Data Eng , vol.25 , pp. 1445-1459
    • Hajian, S.1    Domingo-Ferrer, J.2
  • 30
    • 84884795623 scopus 로고    scopus 로고
    • Practical ensemble classification error bounds for different operating points
    • Varshney KR, Prenger RJ, Marlatt TL, et al. Practical ensemble classification error bounds for different operating points. IEEE Trans Knowl Data Eng. 2013;25:2590-2601.
    • (2013) IEEE Trans Knowl Data Eng , vol.25 , pp. 2590-2601
    • Varshney, K.R.1    Prenger, R.J.2    Marlatt, T.L.3
  • 31
    • 84924757988 scopus 로고    scopus 로고
    • Beat the machine: Challenging humans to find a predictive model's ''unknown unknowns ''
    • Attenberg J, Ipeirotis P, Provost F. Beat the machine: challenging humans to find a predictive model's ''unknown unknowns. '' ACM J Data Inf Qual. 2015;6:1.
    • (2015) ACM J Data Inf Qual , vol.6 , pp. 1
    • Attenberg, J.1    Ipeirotis, P.2    Provost, F.3
  • 32
    • 20844458491 scopus 로고    scopus 로고
    • Mining with rarity: A unifying framework
    • Weiss GM. Mining with rarity: A unifying framework. SIGKDD Explorations Newsletter 2004;6:7-19.
    • (2004) SIGKDD Explorations Newsletter , vol.6 , pp. 7-19
    • Weiss, G.M.1
  • 37
    • 84944705350 scopus 로고    scopus 로고
    • Surgical robotics beyond enhanced dexterity instrumentation: A survey of machine learning techniques and their role in intelligent and autonomous surgical actions
    • Kassahun Y, Yu B, Tibebu AT, et al. Surgical robotics beyond enhanced dexterity instrumentation: A survey of machine learning techniques and their role in intelligent and autonomous surgical actions. Int J Comput Assist Radiol Surg. 2016;11:553-568.
    • (2016) Int J Comput Assist Radiol Surg , vol.11 , pp. 553-568
    • Kassahun, Y.1    Yu, B.2    Tibebu, A.T.3
  • 39
    • 33845273114 scopus 로고    scopus 로고
    • Towards automatic skill evaluation: Detection and segmentation of robot-assisted surgical motions
    • Lin HC, Shafran I, Yuh D, Hager GD. Towards automatic skill evaluation: Detection and segmentation of robot-assisted surgical motions. Comput Aided Surg. 2006;11:220-230.
    • (2006) Comput Aided Surg , vol.11 , pp. 220-230
    • Lin, H.C.1    Shafran, I.2    Yuh, D.3    Hager, G.D.4
  • 41
  • 43
    • 84924662745 scopus 로고    scopus 로고
    • A chance-constrained programming approach to preoperative planning of robotic cardiac surgery under tasklevel uncertainty
    • Azimian H, Naish MD, Kiaii B, Patel RV. A chance-constrained programming approach to preoperative planning of robotic cardiac surgery under tasklevel uncertainty. IEEE Trans Biomed Health Inf. 2015;19:612-1898.
    • (2015) IEEE Trans Biomed Health Inf , vol.19 , pp. 612-1898
    • Azimian, H.1    Naish, M.D.2    Kiaii, B.3    Patel, R.V.4
  • 44
    • 85065292677 scopus 로고    scopus 로고
    • (accessed September 8, 2017)
    • Rayej S. 2014. How do self-driving cars work? Available online at http:// robohub. org/how-do-self-driving-cars-work/ (accessed September 8, 2017).
    • (2014) How Do Self-driving Cars Work
    • Rayej, S.1
  • 47
    • 85015424903 scopus 로고    scopus 로고
    • Cognitive computing safety: The new horizon for reliability
    • Zhu Y, Janapa Reddi V. Cognitive computing safety: The new horizon for reliability. IEEE Micro. 2017;37:15-21.
    • (2017) IEEE Micro , vol.37 , pp. 15-21
    • Zhu, Y.1    Janapa Reddi, V.2
  • 48
    • 84982218478 scopus 로고    scopus 로고
    • Challenges in autonomous vehicle testing and validation
    • Koopman P, Wagner M. Challenges in autonomous vehicle testing and validation. SAE Int J Transportation Saf. 2016;4:2016-01-0128.
    • (2016) SAE Int J Transportation Saf , vol.4 , pp. 2016-010128
    • Koopman, P.1    Wagner, M.2
  • 53
    • 85065297195 scopus 로고    scopus 로고
    • Predictive modeling of customer repayment for sustainable pay-as-you-go solar power in rural India
    • New York, NY
    • Gerard H, Rao K, Simithraaratchy M, et al. Predictive modeling of customer repayment for sustainable pay-as-you-go solar power in rural India. In: Proceedings of Data for Good Exchange Conf., New York, NY2015.
    • (2015) Proceedings of Data for Good Exchange Conf.
    • Gerard, H.1    Rao, K.2    Simithraaratchy, M.3
  • 54
    • 84998764325 scopus 로고    scopus 로고
    • European Union regulations on algorithmic decision-making and a'right to explanation'
    • New York, NY
    • Goodman B, Flaxman S. European Union regulations on algorithmic decision-making and a'right to explanation'. In: Proceedings of ICML Workshop Human Interpretability, New York, NY. 2016, pp. 26-30.
    • (2016) Proceedings of ICML Workshop Human Interpretability , pp. 26-30
    • Goodman, B.1    Flaxman, S.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.