Venger, A.L.; Dozortsev, V.M. Trust in Artificial Intelligence: Modeling the Decision Making of Human Operators in Highly Dangerous Situations. Mathematics2023, 11, 4956.
Venger, A.L.; Dozortsev, V.M. Trust in Artificial Intelligence: Modeling the Decision Making of Human Operators in Highly Dangerous Situations. Mathematics 2023, 11, 4956.
Venger, A.L.; Dozortsev, V.M. Trust in Artificial Intelligence: Modeling the Decision Making of Human Operators in Highly Dangerous Situations. Mathematics2023, 11, 4956.
Venger, A.L.; Dozortsev, V.M. Trust in Artificial Intelligence: Modeling the Decision Making of Human Operators in Highly Dangerous Situations. Mathematics 2023, 11, 4956.
Abstract
Here, we propose a prescriptive simulation model of a process operator’s decision-making assisted by artificial intelligence (AI) algorithm in a technical system control loop. We analyze situations fraught with a catastrophic threat that may cause unacceptable damage. Operators’ decision-making is interpreted in terms of a subjectively admissible probability of disaster and subjectively necessary reliability of its assessment. We distinguish four extreme decision-making strategies corresponding to different ratios between the above variables. An experiment simulating a process facility, an AI algorithm and operator's decision-making strategy was held. It showed that depending on the properties of a controlled process (the speed of hazard onset) and the AI algorithm characteristics (Type I and II error rate), each of such strategies or some intermediate strategy may prove to be more beneficial than others. The same approach is applicable to the identification and analysis of sustainability of strategies applied in real-life operating conditions, as well as to the development of a computer simulator to train operators to control hazardous technological processes using AI-generated advice.
Keywords
human operator; trust in artificial intelligence; recommender systems; intelligent decision-making systems; admissible probability of disaster; and equipment predictive analytics
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
