IEEE Systems, Man and Cybernetics Magazine - July 2022 - 39

[20] R. Tomsett et al., " Rapid trust calibration through interpretable and uncertainty-aware
AI, " Patterns, vol. 1, no. 4, p. 100, 049, 2020, doi: 10.1016/j.patter.
2020.100049.
[21] J. Yuval and P. A. O'Gorman, " Stable machine-learning parameterization of subgrid
processes for climate modeling at a range of resolutions, " Nat. Commun., vol. 11,
no. 1, pp. 1-10, 2020, doi: 10.1038/s41467-020-17142-3.
[22] S. Mozaffari, O. Y. Al-Jarrah, M. Dianati, P. Jennings, and A. Mouzakitis, " Deep
learning-based vehicle behavior prediction for autonomous driving applications: A
review, " IEEE Trans. Intell. Transp. Syst., vol. 23, no. 1, pp. 33-47, Jan. 2020, doi:
10.1109/TITS.2020.3012034.
[23] A. Koohestani, M. Abdar, A. Khosravi, S. Nahavandi, and M. Koohestani, " Integration
of ensemble and evolutionary machine learning algorithms for monitoring diver
behavior using physiological signals, " IEEE Access, vol. 7, pp. 98,971-98,992, 2019, doi:
10.1109/ACCESS.2019.2926444.
[24] M. Abdar et al., " Energy choices in Alaska: Mining people's perception and attitudes
from geotagged tweets, " Renew. Sustain. Energy Rev., vol. 124, p. 109, 781, May
2020, doi: 10.1016/j.rser.2020.109781.
[25] K. Clark, U. Khandelwal, O. Levy, and C. D. Manning, " What does Bert look at? An
analysis of Bert's attention, " 2019, arXiv:1906.04341.
[26] T. Young, D. Hazarika, S. Poria, and E. Cambria, " Recent trends in deep learning
based natural language processing, " IEEE Comput. Intell. Mag., vol. 13, no. 3, pp.
55-75, 2018, doi: 10.1109/MCI.2018.2840738.
[27] B. Wang et al., " Deep uncertainty quantification: A machine learning approach
for weather forecasting, " in Proc. 25th ACM SIGKDD Int. Conf. Knowl. Discovery Data
Mining, Jul. 2019, pp. 2087-2095.
[28] L. Ma and B. Sun, " Machine learning and AI in marketing-Connecting computing
power to human insights, " Int. J. Res. Marketing, vol. 37, no. 3, pp. 481-504, 2020,
doi: 10.1016/j.ijresmar.2020.04.005.
[29] M. Nazari-Heris, B. Mohammadi-Ivatloo, S. Asadi, J. H. Kim, and Z. W. Geem,
" Harmony search algorithm for energy system applications: An updated review
and analysis, " J. Exp. Theor. Artif. Intell., vol. 31, no. 5, pp. 723-749, 2019, doi:
10.1080/0952813X.2018.1550814.
[30] B. T. Pham et al., " Can deep learning algorithms outperform benchmark machine
learning algorithms in flood susceptibility modeling? " J. Hydrol., vol. 592, p. 125, 615,
Jan. 2021, doi: 10.1016/j.jhydrol.2020.125615.
[31] K. R. Larsen and D. S. Becker, Automated Machine Learning for Business. New
York, NY, USA: Oxford Univ. Press, 2021.
[32] W. Lotter et al., " Robust breast cancer detection in mammography and
digital breast tomosynthesis using an annotation-efficient deep learning
approach, " Nat. Med., vol. 27, no. 2, pp. 244-249, 2021, doi: 10.1038/s41591-02001174-9.
[33]
Y. Tolkach, T. Dohmgörgen, M. Toma, and G. Kristiansen, " High-accuracy prostate
cancer pathology using deep learning, " Nat. Mach. Intell., vol. 2, no. 7, pp. 411-418,
2020, doi: 10.1038/s42256-020-0200-7.
[34] A. Kiani et al., " Impact of a deep learning assistant on the histopathologic classification
of liver cancer, " NPJ Digital Med., vol. 3, no. 1, pp. 1-8, 2020, doi: 10.1038/
s41746-020-0232-8.
[35] B. M. Kuenzi et al., " Predicting drug response and synergy using a deep learning
model of human cancer cells, " Cancer Cell, vol. 38, no. 5, pp. 672-684, 2020, doi:
10.1016/j.ccell.2020.09.014.
[36] C. Y. Cheung et al., " A deep-learning system for the assessment of cardiovascular
disease risk via the measurement of retinal-vessel calibre, " Nat. Biomed. Eng., vol. 5,
pp. 498-508, Jun. 2021.
[37] M. Wang et al., " Deep learning-based triage and analysis of lesion burden for
COVID-19: A retrospective study with external validation, " Lancet Digital Health,
vol. 2, no. 10, pp. e506-e515, 2020, doi: 10.1016/S2589-7500(20)30199-0.
[38] H. Singh, A. N. Meyer, and E. J. Thomas, " The frequency of diagnostic errors
in outpatient care: Estimations from three large observational studies involving US
adult populations, " BMJ Qual. Safety, vol. 23, no. 9, pp. 727-731, 2014, doi: 10.1136/
bmjqs-2013-002627.
[39] H. Singh, G. D. Schiff, M. L. Graber, I. Onakpoya, and M. J. Thompson, " The global
burden of diagnostic errors in primary care, " BMJ Qual. Safety, vol. 26, no. 6, pp.
484-494, 2017, doi: 10.1136/bmjqs-2016-005401.
[40] J. G. Richens, C. M. Lee, and S. Johri, " Improving the accuracy of medical diagnosis
with causal machine learning, " Nat. Commun., vol. 11, no. 1, pp. 1-9, 2020.
[41] M. L. Graber, " The incidence of diagnostic error in medicine, " BMJ Qual. Safety,
vol. 22, pp. ii21-ii27, Oct. 2013, doi: 10.1136/bmjqs-2012-001615.
[42] H. Singh, T. D. Giardina, A. N. Meyer, S. N. Forjuoh, M. D. Reis, and E. J. Thomas,
" Types and origins of diagnostic errors in primary care settings, " JAMA Internal Med.,
vol. 173, no. 6, pp. 418-425, 2013, doi: 10.1001/jamainternmed.2013.2777.
[43] S. Benjamens, P. Dhunnoo, and B. Meskó, " The state of artificial intelligencebased
FDA-approved medical devices and algorithms: An online database, " NPJ Digital
Med., vol. 3, no. 1, pp. 1-8, 2020, doi: 10.1038/s41746-020-00324-0.
[44] A. K. Shukla, P. K. Muhuri, and A. Abraham, " A bibliometric analysis and cuttingedge
overview on fuzzy techniques in Big Data, " Eng. Appl. Artif. Intell., vol. 92, p. 103,
625, Jun. 2020, doi: 10.1016/j.engappai.2020.103625.
[45] J. Wilkinson et al., " Time to reality check the promises of machine learning-powered
precision medicine, " Lancet Digital Health, vol. 2, no. 12, pp. e677-e680, 2020,
doi: 10.1016/S2589-7500(20)30200-4.
[46] G. Huang and M. Buss, " Decision-making in machine learning, " in Proc. IEEE Int.
Conf. Adv. Electr. Eng. Comput. Appl. (AEECA), Aug. 2020, pp. 256-260, doi: 10.1109/
AEECA49918.2020.9213677.
[47] D. J. Stracuzzi, M. C. Darling, M. G. Peterson, and M. G. Chen, " Quantifying uncertainty
to improve decision making in machine learning, " Sandia National Lab. (SNLNM),
Albuquerque, NM, USA, No. SAND2018-11166, 2018.
[48] T. Miller, " Explanation in artificial intelligence: Insights from the social sciences, "
Artif. Intell., vol. 267, pp. 1-38, Feb. 2019, doi: 10.1016/j.artint.2018.07.007.
[49] D. Wang, W. Zhang, and B. Y. Lim, " Show or suppress? Managing input uncertainty
in machine learning model explanations, " Artif. Intell., vol. 294, p. 103, 456, 2021, doi:
10.1016/j.artint.2021.103, 456.
[50] A. Holzinger, G. Langs, H. Denk, K. Zatloukal, and H. Müller, " Causability and
explainability of artificial intelligence in medicine, " Wiley Interdisciplinary Rev.,
Data Mining Knowl. Discovery, vol. 9, no. 4, p. e1312, 2019.
[51] J. Jiménez-Luna, F. Grisoni, and G. Schneider, " Drug discovery with explainable
artificial intelligence, " Nature Mach. Intell., vol. 2, no. 10, pp. 573-584, 2020, doi:
10.1038/s42256-020-00236-4.
[52] F. Doshi-Velez and B. Kim, " Towards a rigorous science of interpretable machine
learning, " 2017, arXiv:1702.08608.
[53] A. Kendall and Y. Gal, " What uncertainties do we need in Bayesian deep learning
for computer vision? " 2017, arXiv:1703.04977.
[54] J. M.Roldán, O. P.Vila, and J. V. Marca, " Dirichlet uncertainty wrappers for
actionable algorithm accuracy accountability and auditability, " in Proc. Conf.
Fairness, Accountability, Transparency, Jan. 2020, pp. 581-581.
[55] E. Hüllermeier and W. Waegeman, " Aleatoric and epistemic uncertainty in
machine learning: An introduction to concepts and methods, " Mach. Learn., vol. 110,
no. 3, pp. 457-506, 2021, doi: 10.1007/s10994-021-05946-3.
[56] Y. Gal and Z. Ghahramani, " Dropout as a Bayesian approximation: Representing
model uncertainty in deep learning, " in Proc. Int. Conf. Machine Learning, Jun. 2016,
pp. 1050-1059.
[57] S. E. Kreps and D. L. Kriner, " Model uncertainty, political contestation, and public
trust in science: Evidence from the COVID-19 pandemic, " Sci. Adv., vol. 6, no. 43,
p. eabd4563, 2020, doi: 10.1126/sciadv.abd4563.
July 2022 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE 39
IEEE Systems, Man and Cybernetics Magazine - July 2022

Table of Contents for the Digital Edition of IEEE Systems, Man and Cybernetics Magazine - July 2022
