Industry 4.0
Vol. 11 (2026), Issue 1, pg(s) 34-37

SOCIETY & ”INDUSTRY 4.0”

Artificial intelligence supporting human decision-making in technological crises

  • 1 Bulgarian Academy of Sciences, Sofia, Bulgaria, Institute of Information and Communication Technologies

Abstract

Technological crises refer to critical events—system failures and collapses, cybersecurity and data breaches, software meltdowns, critical infrastructure disruptions, etc.,—in which the failure, malfunction, or disruption of technological systems creates significant risk to safety, operations, or the environment. These crises typically arise unexpectedly and require rapid, well-informed decision-making under conditions of uncertainty, time pressure, and missing or incomplete information, to prevent escalation. Artificial intelligence (AI) systems have emerged as powerful tools capable of augmenting human judgment in these contexts. This article examines the role of AI in supporting human decision-making during technological crises, analyzing its capabilities, limitations, and implications for organizational resilience. It emphasized that AI is most effective when used as a collaborative partner rather than a replacement for human expertise, and it outlines design principles for trustworthy, human-centered AI systems.

Keywords

References

  1. Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D.Concrete problems in AI safety, (2016). arXiv:1606.06565.
  2. Bankes, S. Exploratory modeling for policy analysis. Operations Research, 41(3), (1993), 435–449.
  3. Batarseh, F. A., & Yang, R. AI for data-driven crisis management. Springer. (2021).
  4. Chandola, V., Banerjee, A., & Kumar, V. Anomaly detection: A survey. ACM Computing Surveys, 41(3), (2009), 1–58.
  5. Cummings, M. LMan versus machine or man + machine? IEEE Intelligent Systems, 29(5), (2014), 62–69.
  6. Endsley, M. R. Toward a theory of situation awareness in dynamic systems. Human Factors, 37(1), (1995), 32–64.
  7. Endsley, M. R. Automation and situation awareness. In Handbook of human factors and ergonomics (2018), pp. 533–546
  8. Hollnagel, E., Safety-II in practice: Developing the resilience potentials. Routledge, (2017)
  9. Jordan, M. I., & Mitchell, T. M., Machine learning: Trends, perspectives, and prospects. Science, 349(6245), (2015),255–260.
  10. Kahneman, D., Thinking, fast and slow. Farrar, Straus and Giroux,(2011)
  11. Klein, G.,Naturalistic decision making. Human Factors, 50(3), (2008), 456–460.
  12. Klein, G., Sources of power: How people make decisions. MIT Press., (2017)
  13. Miller, C. A., & Parasuraman, R.,Designing for flexible interaction between humans and automation. Human Factors, 49(1), (2007), 57–71.
  14. Ouyang, M., Review on modeling and simulation of interdependent critical infrastructure systems. Reliability Engineering & System Safety, 121, (2014), 43–60.
  15. Panteli, M., & Mancarella, P. Influence of extreme weather and climate change on the resilience of power systems. IEEE Transactions on Smart Grid, 7(3), (2015), 1–10.
  16. Parasuraman, R., & Riley, V., Humans and automation: Use, misuse, disuse, abuse. Human Factors, 39(2), (1997), 230–253.
  17. Parasuraman, R., Sheridan, T. B., & Wickens, C. D., A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics, 30(3), (2000), 286–297.
  18. Perrow, C., Normal accidents: Living with high-risk technologies. Princeton University Press. (1999)
  19. Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J.-F., Breazeal, C., Wellman, M., Machine behaviour. Nature, 568(7753), (2019), 477–486.
  20. Salfner, F., Lenk, M., & Malek, M. A survey of online failure prediction methods. ACM Computing Surveys, 42(3), (2010), 1–42.
  21. Sheridan, T. B., Human–robot interaction: Status and challenges. Human Factors, 58(4), (2016), 525–532.
  22. Shneiderman, B. Human Centered AI. Oxford University Press, Oxford,UK, (2022),111-137; 145-179

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