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Citation |
Vink LS. Transp. Res. Proc. 2022; 66: 201-213. |
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Copyright |
(Copyright © 2022, Elsevier Publications) |
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DOI |
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PMID |
unavailable |
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Abstract |
The traditional approaches to classifying Human Error have provided a strong and beneficial approach to understanding the roles of humans in safety occurrences. However, as the demand for human performance has increased and with the rise of more automation, the line between man and machine continues to become increasingly blurred. Traditional Human Error analysis techniques are no longer sufficient for extracting the most valuable learning data from occurrences. Furthermore, there is a continuing need to strengthen Safety and Just culture. One means to achieving this is through developing ever greater trust in the ability of organisations to accurately investigate and report occurrences. This paper outlines a new 10 step approach to investigating human contribution to occurrences which is based on a version of predictive situational awareness in the brain that is derived from hierarchical predictive coding approaches. Utilising statistically based induction, the steps allow investigators to make an objective reconstruction of sub-conscious human error analysis and integrate it with traditional subjective feedback on conscious decisions made. Examples of system data that can provide further evidence to support the reconstructions is also discussed. This approach has been tested on more than 50 human factors related occurrences in Air Traffic Control in 2021/2022 and the results are introduced. It is further argued that using a refined approach and educating operators on the approach can significantly benefit Just Culture and lead to increases in trust and frequency of reporting because it will allow for the removal of biases by individual investigators. Future research should focus on validating the approach against current methods and testing it for occurrences further away from front line operators such as in non-safety related decision making. Language: en |
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Keywords |
Bayesian hierarchical predictive coding; Cognitive models; Human Error Analysis; Human Performance; Just Culture |