Identification of human errors and influencing factors : a machine learning approach

Tools

Morais, Caroline and Yung, Ka Lai and Johnson, Karl and Moura, Raphael and Beer, Michael and Patelli, Edoardo (2022) Identification of human errors and influencing factors : a machine learning approach. Safety Science, 146. p. 105528. 105528. ISSN 0925-7535 (https://doi.org/10.1016/j.ssci.2021.105528)

[thumbnail of Morais-etal-SS-2021-Identification-of-human-errors-and-influencing-factors-a-machine-learning-approach]
Preview
 Text. Filename: Morais_etal_SS_2021_Identification_of_human_errors_and_influencing_factors_a_machine_learning_approach.pdf
Accepted Author Manuscript
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo
Download (1MB)| Preview

Abstract

The capability of learning from accidents from different industrial sectors could prevent similar accidents to happen. With this aim, the Multi-attribute Technological Accidents Dataset (MATA-D) has been created, using a classification focused on the relation between human errors and their influencing factors (e.g., cognitive functions, organisational and technological factors). The process of collecting new data for this dataset should be constant, not only to decrease epistemic uncertainty in human reliability data but also to reflect changes in human behaviour due to evolving technology and organisational arrangements. However, reading an accident report is a time-consuming process, which delays the learning process. For this reason, this research proposes an automated approach to train the computer on a predefined classification scheme (taxonomy), which will be called the virtual human factors classifier. The virtual classifier should support human experts to analyse accident reports for organisational, technological, and individual factors that may trigger human errors. The proposed approach is based on classifying text according to previously labelled accident reports by human experts. Two case studies are used to demonstrate how data from different sectors can be used to train the machine, providing an efficient cross-discipline knowledge transfer. The accuracy of the results is promising and comparable to the classifications provided by human experts. The proposed work demonstrated to the industry the feasibility of the use of artificial intelligence to collect data and support risk and reliability assessments, and recommendations based on the study findings are suggested for investigation agencies.

ORCID iDs

Morais, Caroline, Yung, Ka Lai, Johnson, Karl, Moura, Raphael, Beer, Michael and Patelli, Edoardo ORCID logoORCID: https://orcid.org/0000-0002-5007-7247;
CORE (COnnecting REpositories)