Human vs machine judgment in safety-critical logistics: a philosophical inquiry into trust and control
DOI:
https://doi.org/10.5281/zenodo.20402507Keywords:
safety-critical systems, aritificial intelligence, decision-making, trust in technology, logisticsAbstract
This article examines the differences between human judgment and algorithmic decision-making in critical logistics systems, focusing on the implications for the concepts of trust and control. The aim of the study is to determine whether AI-based systems can perform functions equivalent to human judgment and what implications this has for operational safety and accountability in complex logistics systems. The study is based on an interdisciplinary analysis, including a review of the literature on artificial intelligence, system safety, and human factors research, as well as a philosophical analysis. This is supplemented by a case study of selected incidents related to the operation of automated systems in high-risk environments, which allows the identification of practical limitations in integrating humans and algorithmic systems. The results indicate that decisions generated by algorithmic systems are statistical and optimization-based, which distinguishes them from human judgment, which takes into account context, uncertainty, and emotions. Consequently, trust in AI systems takes the form of an indirect, user-dependent relationship rather than an autonomous property of the technology. At the same time, increasing levels of automation lead to a diffusion of responsibility and a reduction in direct control over decision-making processes. The findings indicate that full automation of decisions in critical safety systems. Consequently, trust in AI systems takes the form of an indirect, user-dependent relationship rather than an autonomous property of the technology.
References
1. Amalberti, R. (2001) ‘The paradoxes of almost totally safe transportation systems’, Safety Science, 37, pp. 109–126. doi: 10.1016/S0925-7535(00)00045-X.
2. Busuioc, M. (2020) ‘Accountable artificial intelligence: Holding algorithms to account’, Public Administration Review, 81, pp. 825–836. doi: 10.1111/puar.13293.
3. Curran, N., Kennings, T. and Shin, K. (2024) ‘Analysis and prevention of MCAS-induced crashes’, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 43, pp. 3382–3394. doi: 10.1109/TCAD.2024.3438105.
4. De Morais, G., Giollo, C., Júnior, M. and Villani, E. (2025) ‘Improving decision-making of civilian and military pilots under pressure’, Linköping Electronic Conference Proceedings. doi: 10.3384/wcc215.1187.
5. Ezvan, C. and Ottaviani, F. (2026) ‘Embodied universalism: An Aristotelian perspective for evaluating the good life’, Ecological Economics, 244. p. 108946. doi: 10.1016/j.ecolecon.2026.108946.
6. Festor, P. et al. (2025) ‘Safety of human-AI cooperative decision-making within intensive care: A physical simulation study’, PLOS Digital Health, 4. p. e0000726. doi: 10.1371/journal.pdig.0000726.
7. Gu, P. (2024) ‘The flash crash: The impact of high-frequency trading on the stability of financial market’, Highlights in Business, Economics and Management, 24, pp. 1158–1163. doi: 10.54097/mc1h9z31.
8. Gigerenzer, G. (2007) Gut feelings: The intelligence of the unconscious. New York: Viking.
9. Häggström, C. (2015) Human factors in mechanized cut-to-length forest operations. Uppsala: Swedish University of Agricultural Sciences.
10. Holska, A. (2016) ‘Teorie podejmowania decyzji’, in Klincewicz, K. (ed.) Zarządzanie, organizacje i organizowanie – przegląd perspektyw teoretycznych. Warsaw: Wydawnictwo Naukowe WZ UW, pp. 239–252. doi: 10.7172/978-83-65402-29-5.2016.wwz.9.
11. Hong, N. et al. (2022) ‘State of the art of machine learning–enabled clinical decision support in intensive care units: Literature review’, JMIR Medical Informatics, 10, e28781. doi: 10.2196/28781.
12. Kant, I. (1987) The critique of judgment. Indianapolis: Hackett Publishing Company.
13. Kahneman, D. (2011) Thinking, fast and slow. New York: Farrar, Straus and Giroux.
14. Königs, P. (2022) ‘Artificial intelligence and responsibility gaps: What is the problem?’, Ethics and Information Technology, 24(36). doi: 10.1007/s10676-022-09643-0.
15. Lee, J.D. and See, K.A. (2004) ‘Trust in automation: Designing for appropriate reliance’, Human Factors, 46, pp. 50–80. doi: 10.1518/hfes.46.1.50_30392.
16. Leveson, N.G. (2012) Engineering a safer world: Systems thinking applied to safety. Cambridge, MA: MIT Press.
17. Martins, E. and Soares, M. (2012) ‘Automation under suspicion – case flight AF447 Air France’, Work, 41, pp. 222–224. doi: 10.3233/WOR-2012-0160-222.
18. Parasuraman, R., Sheridan, T.B. and Wickens, C.D. (2000) ‘A model for types and levels of human interaction with automation’, IEEE Transactions on Systems, Man, and Cybernetics – Part A, 30, pp. 286–297. doi: 10.1109/3468.844354.
19. Reason, J. (1990) Human error. Cambridge: Cambridge University Press.
20. Russell, S. and Norvig, P. (2022) Artificial intelligence: A modern approach 4th edn. Pearson.
21. Saunders, F. (2015) ‘Toward high reliability project organizing in safety-critical projects’, Project Management Journal, 46, pp. 25–35. doi: 10.1002/pmj.21498.
22. Sawyerr, E. and Harrison, C. (2019) ‘Developing resilient supply chains: Lessons from high-reliability organisations’, Supply Chain Management, 25(1), pp. 77–100. doi: 10.1108/SCM-09-2018-0329.
23. Skitka, L.J., Mosier, K.L. and Burdick, M.D. (2000) ‘Accountability and automation bias’, International Journal of Human-Computer Studies, 52(4), pp. 701–717. doi: 10.1006/ijhc.1999.0349.
24. Todd, P.M. and Gigerenzer, G. (2000) ‘Précis of simple heuristics that make us smart’, Behavioral and Brain Sciences, 23, pp. 727–780.
25. Verdiesen, I., De Sio, F. and Dignum, V. (2020) ‘Accountability and control over autonomous weapon systems: A framework for comprehensive human oversight’, Minds and Machines, 31, pp. 137–163. doi: 10.1007/s11023-020-09532-9.
26. Weick, K.E. and Sutcliffe, K.M. (2011) Managing the unexpected: Resilient performance in an age of uncertainty. 2nd edn. Hoboken, NJ: Wiley.
27. Xiong, W., Fan, H., Ma, L. and Wang, C. (2022) ‘Challenges of human–machine collaboration in risky decision-making’, Frontiers of Engineering Management, 9, pp. 89–103. doi: 10.1007/s42524-021-0182-0.
28. Zerilli, J. et al. (2019) ‘Algorithmic decision-making and the control problem’, Minds and Machines, 29, pp. 555–579. doi: 10.1007/s11023-019-09513-7.
29. House Transportation and Infrastructure (2020) Hearing on Boeing 737 MAX. Available at: https://www.congress.gov/event/116th-congress/house-event/110066/text/
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