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

Gursel, Ezgi; Madadi, Mahboubeh; Coble, Jamie Baalis; Agarwal, Vivek; Yadav, Vaibhav; Boring, Ronald L.; Khojandi, Anahita

doi:10.1016/j.ress.2024.110682

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

Journal Article · 27 November 2024 · Reliability Engineering and System Safety

DOI: https://doi.org/10.1016/j.ress.2024.110682 · OSTI ID:2479887

Gursel, Ezgi ^[1]; Madadi, Mahboubeh ^[2]; Coble, Jamie Baalis ^[1]; Agarwal, Vivek ^[3]; Yadav, Vaibhav ^[3]; Boring, Ronald L. ^[3];

^[1]

University of Tennessee, Knoxville, TN (United States)
University of Tennessee, San Jose, CA (United States)
Idaho National Laboratory (INL), Idaho Falls, ID (United States)

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. Furthermore, 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.

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Research Organization:: Idaho National Laboratory (INL), Idaho Falls, ID (United States); University of Tennessee, Knoxville, TN (United States)

Sponsoring Organization:: USDOE; USDOE Office of Nuclear Energy (NE), Nuclear Energy University Program (NEUP)

Grant/Contract Number:: AC07-05ID14517; NE0008978

OSTI ID:: 2479887

Report Number(s):: INL/JOU--24-76319-Rev000

Journal Information:: Reliability Engineering and System Safety, Journal Name: Reliability Engineering and System Safety Vol. 256; ISSN 0951-8320

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework Wang, Chen; Liu, Lin; Xu, Chengcheng International Journal of Environmental Research and Public Health, Vol. 16, Issue 3 https://doi.org/10.3390/ijerph16030334	journal	January 2019
Fusing XGBoost and SHAP Models for Maritime Accident Prediction and Causality Interpretability Analysis Zhang, Cheng; Zou, Xiong; Lin, Chuan Journal of Marine Science and Engineering, Vol. 10, Issue 8 https://doi.org/10.3390/jmse10081154	journal	August 2022
Digital Twin Technology Challenges and Applications: A Comprehensive Review Botín-Sanabria, Diego M.; Mihaita, Adriana-Simona; Peimbert-García, Rodrigo E. Remote Sensing, Vol. 14, Issue 6 https://doi.org/10.3390/rs14061335	journal	March 2022
A Review of Data Analytic Applications in Road Traffic Safety. Part 2: Prescriptive Modeling Hu, Qiong; Cai, Miao; Mohabbati-Kalejahi, Nasrin Sensors, Vol. 20, Issue 4 https://doi.org/10.3390/s20041096	journal	February 2020
Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach Nallathambi, Indumathi; Savaram, Padmaja; Sengan, Sudhakar Sensors, Vol. 23, Issue 9 https://doi.org/10.3390/s23094365	journal	April 2023

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