Trust in Artificial Intelligence: Modeling the Decision Making of Human Operators in Highly Dangerous Situations
A prescriptive simulation model of a process operator’s decision making assisted with an artificial intelligence (AI) algorithm in a technical system control loop is proposed. Situations fraught with a catastrophic threat that may cause unacceptable damage were analyzed. The operators’ decision maki...
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/24/4956 |
| _version_ | 1797380139824185344 |
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| author | Alexander L. Venger Victor M. Dozortsev |
| author_facet | Alexander L. Venger Victor M. Dozortsev |
| author_sort | Alexander L. Venger |
| collection | DOAJ |
| description | A prescriptive simulation model of a process operator’s decision making assisted with an artificial intelligence (AI) algorithm in a technical system control loop is proposed. Situations fraught with a catastrophic threat that may cause unacceptable damage were analyzed. The operators’ decision making was interpreted in terms of a subjectively admissible probability of disaster and subjectively necessary reliability of its assessment, which reflect the individual psychological aspect of operator’s trust in AI. Four extreme decision-making strategies corresponding to different ratios between the above variables were distinguished. 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 (its dynamics and the hazard onset’s speed) 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. |
| first_indexed | 2024-03-08T20:34:00Z |
| format | Article |
| id | doaj.art-90d75cf54ae64f0c921c8463ea012266 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-08T20:34:00Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-90d75cf54ae64f0c921c8463ea0122662023-12-22T14:23:25ZengMDPI AGMathematics2227-73902023-12-011124495610.3390/math11244956Trust in Artificial Intelligence: Modeling the Decision Making of Human Operators in Highly Dangerous SituationsAlexander L. Venger0Victor M. Dozortsev1Department of Social Sciences and Humanities, Dubna State University, 141982 Dubna, RussiaMoscow Institute of Physics and Technology (MIPT), 117303 Moscow, RussiaA prescriptive simulation model of a process operator’s decision making assisted with an artificial intelligence (AI) algorithm in a technical system control loop is proposed. Situations fraught with a catastrophic threat that may cause unacceptable damage were analyzed. The operators’ decision making was interpreted in terms of a subjectively admissible probability of disaster and subjectively necessary reliability of its assessment, which reflect the individual psychological aspect of operator’s trust in AI. Four extreme decision-making strategies corresponding to different ratios between the above variables were distinguished. 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 (its dynamics and the hazard onset’s speed) 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.https://www.mdpi.com/2227-7390/11/24/4956human operatortrust in artificial intelligencerecommender systemsintelligent decision-making systemsadmissible probability of disasterequipment predictive analytics |
| spellingShingle | Alexander L. Venger Victor M. Dozortsev Trust in Artificial Intelligence: Modeling the Decision Making of Human Operators in Highly Dangerous Situations Mathematics human operator trust in artificial intelligence recommender systems intelligent decision-making systems admissible probability of disaster equipment predictive analytics |
| title | Trust in Artificial Intelligence: Modeling the Decision Making of Human Operators in Highly Dangerous Situations |
| title_full | Trust in Artificial Intelligence: Modeling the Decision Making of Human Operators in Highly Dangerous Situations |
| title_fullStr | Trust in Artificial Intelligence: Modeling the Decision Making of Human Operators in Highly Dangerous Situations |
| title_full_unstemmed | Trust in Artificial Intelligence: Modeling the Decision Making of Human Operators in Highly Dangerous Situations |
| title_short | Trust in Artificial Intelligence: Modeling the Decision Making of Human Operators in Highly Dangerous Situations |
| title_sort | trust in artificial intelligence modeling the decision making of human operators in highly dangerous situations |
| topic | human operator trust in artificial intelligence recommender systems intelligent decision-making systems admissible probability of disaster equipment predictive analytics |
| url | https://www.mdpi.com/2227-7390/11/24/4956 |
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