The latest developments in AI have the potential to significantly support qualitative analysis of interview transcripts. This study explores the utility of the OpenAI o1-model to assist in efficiently obtaining reliable summarizations of safety culture interviews. Analysis shows that the current approach has the clear potential to significantly improve efficiency of interviewers by providing a concise report that summarizes multiple interviews according to a pre-defined format. However, some hallucinations are present in the generated report. Additional work will aim at reducing their presence, but such hallucinations also emphasizes that LLMs should primarily assist, rather than replace, interviewers in creating a definitive report.

Quantitative Risk Analysis (QRA) is a standard tool in some high-risk industries (such as the on- and offshore exploration and production and chemical industry). Presently, existing knowledge concerning human error likelihood and human reliability assessment is insufficiently represented in QRAs. In this paper we attempt to implement the quantification of the human factors in a QRA, which we call QRA+. We analysed a specific incident scenario: the risk of overfilling chemical storage tanks that operate at atmospheric pressure. This scenario was chosen because it is a relevant example of a high-risk scenario in the chemical industry. We identified relevant technological and human parameters within this scenario through on-site visits and interviews with site-experts. The quantitative knowledge concerning the technological parameters was obtained from officially documented SIL statistics, whereas the Standardized Plant Analysis Risk-Human Reliability analysis (SPAR-H) was used to quantify the human factors. Beta distributions were used to model failure probability distributions to account for the uncertainty inherent in dealing with human reliability. For seamless integration of existing qualitative and quantitative knowledge, we made use of a Bayesian Belief Network. The resulting model provides an integrated and more accurate estimation of the failure probabilities for both technological and human factors and the uncertainty surrounding such probability estimates. Furthermore, it gives insight in where these failure probabilities originate and how they interact. This will allow companies to identify those parameters they need to influence to get optimal results concerning their management of risk.

Last minute risk assessment (LMRA) is a well-known work method to support employees’ risk perception. However, little is known about the effectiveness of LMRA in providing this support. Here, we describe an eye-tracking experiment with which we attempted to gain more insight into the relationship between LMRA and risk perception and to assess the difference between generic and specific supporting questions. Employees from an international energy production and desalination company participated in this experiment by assessing photographs portraying a (staged) work situation and deciding whether it was safe enough to continue activities and which risk factors were present or absent. The results show a consistent interaction effect over several parameters between work experience and the type of supporting questions, indicating that generic and specific supporting questions should be considered complimentary to each other. Furthermore, the results revealed several other challenges concerning real-world application of the LMRA.