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The role of AI in detecting and mitigating human errors in safety-critical industries: A review.

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Title: The role of AI in detecting and mitigating human errors in safety-critical industries: A review.
Authors: Gursel, Ezgi1 (AUTHOR), Madadi, Mahboubeh2 (AUTHOR), Coble, Jamie Baalis3 (AUTHOR), Agarwal, Vivek4 (AUTHOR), Yadav, Vaibhav4 (AUTHOR), Boring, Ronald L.4 (AUTHOR), Khojandi, Anahita1 (AUTHOR) khojandi@utk.edu
Source: Reliability Engineering & System Safety. Apr2025, Vol. 256, pN.PAG-N.PAG. 1p.
Subjects: Human error, Artificial intelligence, Machine learning, Prediction models
Abstract: • This review considers AI/ML for human errors in safety-critical industries. • Studies are categorized into descriptive, predictive, prescriptive, generative modeling types. • Findings suggest AI/ML can be utilized to help with human error in safety-critical industries. For safety-critical industries, human error (HE) presents continual risks to system productivity, reliability and safety. Artificial intelligence (AI) and machine learning (ML) methods have emerged as promising approaches to understand, categorize and mitigate the risk of HE in safety-critical industries. This review offers an examination of the current landscape regarding the utilization of AI/ML with regards to HE in safety-critical industries, categorizing literature into descriptive modeling, predictive modeling, prescriptive modeling, and generative modeling techniques. Additionally, the review aims to provide insights regarding themes in literature, challenges, and future research directions. Findings of the review suggest that AI/ML methods can prove useful in addressing the HE problem across safety-critical industries. [ABSTRACT FROM AUTHOR]
Copyright of Reliability Engineering & System Safety is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
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DbLabel: Engineering Source
An: 182854885
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PubType: Academic Journal
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  Data: The role of AI in detecting and mitigating human errors in safety-critical industries: A review.
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  Data: <searchLink fieldCode="JN" term="%22Reliability+Engineering+%26+System+Safety%22">Reliability Engineering & System Safety</searchLink>. Apr2025, Vol. 256, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Human+error%22">Human error</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink>
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  Data: • This review considers AI/ML for human errors in safety-critical industries. • Studies are categorized into descriptive, predictive, prescriptive, generative modeling types. • Findings suggest AI/ML can be utilized to help with human error in safety-critical industries. For safety-critical industries, human error (HE) presents continual risks to system productivity, reliability and safety. Artificial intelligence (AI) and machine learning (ML) methods have emerged as promising approaches to understand, categorize and mitigate the risk of HE in safety-critical industries. This review offers an examination of the current landscape regarding the utilization of AI/ML with regards to HE in safety-critical industries, categorizing literature into descriptive modeling, predictive modeling, prescriptive modeling, and generative modeling techniques. Additionally, the review aims to provide insights regarding themes in literature, challenges, and future research directions. Findings of the review suggest that AI/ML methods can prove useful in addressing the HE problem across safety-critical industries. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Reliability Engineering & System Safety is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.ress.2024.110682
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        Text: English
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        Type: general
      – SubjectFull: Artificial intelligence
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      – SubjectFull: Machine learning
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              Text: Apr2025
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              Y: 2025
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