Abstract
With rising complexity of work systems, occupational safety experts need to keep up with risk analysis, while smaller enterprises furthermore struggle due to a lack of specialist personnel and due to absence of systematic safety processes. To alleviate this problem, we propose the use of state-of-the-art Machine Learning techniques to support occupational safety risk analysis in enterprises.
In this paper we provide an overview over the inherent challenges of such a system, encompassing data availability and extraction, handling of missing, heterogeneous or just too sparse data with appropriate algorithms and their variants, and User Interface aspects including explainability.
We map out the next steps in the process of implementing a real world application to support occupational safety experts in a large enterprise to prove the feasibility of such an approach and to gather experience, which can eventually be drawn upon to develop a generalized tool, if possible for all the stakeholders present in the occupational safety process.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Mattei, P.-A., Frellsen, J.: MIWAE: deep generative modelling and imputation of incomplete data sets. In: International Conference on Machine Learning. PMLR (2019)
Mirza, B., et al.: Machine learning and integrative analysis of biomedical big data. Genes 10(2), 87 (2019)
Clemen, R.T., Winkler, R.L.: Combining probability distributions from experts in risk analysis. Risk Anal. 19(2), 187–203 (1999)
Wang, Y., et al.: Generalizing from a few examples: a survey on few-shot learning. ACM Comput. Surv. (CSUR) 53(3), 1–34 (2020)
Dellermann, D., et al.: Hybrid intelligence. Bus. Inf. Syst. Eng. 61(5), 637–643 (2019)
Aven, T.: An emerging new risk analysis science: foundations and implications. Risk Anal. 38(5), 876–888 (2018)
Joy, J.: Occupational safety risk management in Australian mining. Occup. Med. 54(5), 311–315 (2004)
O’Beirne, T., Napper, A.: Introduction of systematic safety assessment techniques to underground coal industry (1990)
Tixier, J., et al.: Review of 62 risk analysis methodologies of industrial plants. J. Loss Prev. Process Ind. 15(4), 291–303 (2002)
Amin, M.T., Khan, F., Amyotte, P.: A bibliometric review of process safety and risk analysis. Process Saf. Environ. Prot. 126, 366–381 (2019)
De Silva, N., Ranasinghe, M., De Silva, C.R.: Risk analysis in maintainability of high-rise buildings under tropical conditions using ensemble neural network. Facilities 34( 1/2), 2–27 (2016). https://doi.org/10.1108/F-05-2014-0047
Kittelmann, M., Adolph, L., Michel, A., Packroff, R., Schütte, M., Sommer, S. (Hrsg.): Handbuch Gefährdungsbeurteilung, 1st edn. Bundesanstalt für Arbeitsschutz und Arbeitsmedizin, Dortmund (2022)
BAuA. Handbuch Gefährdungsbeurteilung Teil 2: Gefährdungsfaktoren (2021). https://www.baua.de/DE/Themen/Arbeitsgestaltung-im-Betrieb/Gefaehrdungsbeurteilung/Expertenwissen/Expertenwissen_node.html. Accessed 17 Oct 2021
Dollard, M.F., et al.: Psychosocial safety climate (PSC) and enacted PSC for workplace bullying and psychological health problem reduction. Eur. J. Work Organ. Psy. 26(6), 844–857 (2017)
Shams, R.: Semi-supervised classification for natural language processing. arXiv preprint arXiv:1409.7612 (2014)
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)
Doran, D., Schulz, S., Besold, T.R.: What does explainable AI really mean? A new conceptualization of perspectives. arXiv preprint arXiv:1710.00794 (2017)
Schmidt, A., Herrmann, T.: Intervention user interfaces: a new interaction paradigm for automated systems. Interactions 24(5), 40–45 (2017)
Shneiderman, B., Plaisant, C.: Designing the user Interface: Strategies for Effective Human-Computer Interaction. Pearson Education India (2010)
Herrmann, T.: Socio-technical design of hybrid intelligence systems – the case of predictive maintenance. In: Degen, H., Reinerman-Jones, L. (eds.) HCII 2020. LNCS, vol. 12217, pp. 298–309. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50334-5_20
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Westhoven, M., Adolph, L. (2022). Concept for Supporting Occupational Safety Risk Analysis with a Machine Learning Tool. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2022 Posters. HCII 2022. Communications in Computer and Information Science, vol 1580. Springer, Cham. https://doi.org/10.1007/978-3-031-06417-3_63
Download citation
DOI: https://doi.org/10.1007/978-3-031-06417-3_63
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-06416-6
Online ISBN: 978-3-031-06417-3
eBook Packages: Computer ScienceComputer Science (R0)