## 1 Introduction
The use of machine learning and big data analytics has become an innovative approach to predicting human errors based on historical data. Machine learning allows for real-time modeling and classification of errors and can provide accurate risk predictions based on behavioral patterns and circumstances. This approach is starting to be used in various industries such as aviation, nuclear energy, and the automotive industry, where human behavior is analyzed in real-world conditions [6]. Virtual and augmented reality-based simulations are increasingly being used to test workers' responses in simulated risky situations. Using VR, different scenarios can be modeled to test decision-making processes and interactions between people and technology. This approach not only enables training but also allows for the assessment of how human behavior can impact the overall reliability of the system [7]. Cognitive Task Analysis (CTA) is a technique that focuses on a detailed understanding of individual decision-making processes while performing tasks. Models such as Cognitive Reliability and Error Analysis Method (CREAM) provide a framework for assessing human errors in complex systems. These models are used to identify cognitive errors that may affect system reliability [8]. Bayesian networks are probabilistic models that allow for the analysis of risks and prediction of human errors in various scenarios. This method is used to evaluate dynamic interactions between human factors, technology, and the environment,while accounting for uncertainty and variability in human behavior [9]. The latest trend in the concept of \"Safety Culture\" focuses on evaluating how organizational factors (e.g., communication, culture, and leadership) influence individual behavior in safety matters. These approaches are based on the assumption that human behavior is influenced not only by individual factors but also by the broader organizational context [10]. HRA is a discipline that offers methods and tools for both qualitative and quantitative assessment of human errors in systems where people perform monitoring and/or control functions [11]. Although HRA methods are widely used, especially in the nuclear sector, they were specifically developed for probabilistic risk assessment in the assembly of nuclear weapons in the 1960s [12]. In 1982, [PERSON] [13] presented a taxonomy aimed at assessing differences in the performance of specific tasks. This behavioral model (based on skills, rules and knowledge--SRK) includes the assessment of three categories:  
* Capability-based errors;
* Knowledge-based errors;
* Rule-based errors. [PERSON]'s research verified that errors due to lack of skills accounted for 16.5% of the total failure rate, errors due to rule violations accounted for 51% and errors based on lack of knowledge accounted for 22.5%. The Human Reliability Assessment (HRA) is based on a detailed description of the functions, roles and resources in the human-environment relationship. The procedure is based on partial analyses [14]:  
* Task Analysis--TA;
* Human Error Identification--HEI;
* Quantification of human factor reliability or Human Error Probability--HEP. HRA was subsequently adapted for risk assessment in nuclear power plants and integrated into the nuclear regulatory framework. It focused on identifying, assessing, and mitigating risks associated with human errors in nuclear power plants [15; 16]. Currently, HRA is used in various safety-critical sectors, such as aviation, railway transportation, public transportation, the oil and gas industry, as well as in other sectors like construction and agriculture [17; 18]. A study by the UK Health and Safety Executive identified 17 Human Reliability Assessment methods that could potentially be useful for authoritative organizations performing high-risk activities. The study focuses on reviewing and evaluating various HRA methodologies that can be applied to identify and minimize risks in high-safety potential systems, such as those in the nuclear, chemical, and aviation industries [19].