## 1 Introduction  
The immense need of contemporary industries to meet the technological requirements of the factories of the future as imposed by Industry 4.0 brought significant technological breakthroughs in the robotics domain, leading the robots out of their cages to work in close collaboration with humans, aiming to increase productivity, flexibility and autonomy in production [1]. Although the fact that such collaborative robots are designed and built with inherent safety mechanisms [2], their deployment in unconstrained environments typically necessitates their integration with other technological tools, such as visual and non-visual sensors along with custom algorithm solutions and other human machines interaction (HMI) interfaces, to realize true collaboration with human co-workers. The synergy of all these components formulate a collaborative ecosystem that, combined with the human interactive actions, could provoke hazardous events that conditionally render human-robot collaboration (HRC) unsafe [3]. This new type of collaborative environment imposed the in-depth re-consideration of safety mechanisms that take into account both the new technological advancements and the human factor, which holds an important role in Industry 4.0, to enable the workflow's execution as smoothly as possible [4]. Towards the design of such a system of human-robot collaboration, the discussions with experts and company workers is also needed [5, 6]. On the one hand, the safety community brought into action specific International Organization for Standardization (ISO) standards [7, 8], custom tailored to identify a series of important hazards in HRC applications including potential sources of harm for the operator, their likely origins, and safety regulations for guiding the design and deployment of robotic solutions [9]. In particular, ISO standard 12100 [10] has been designed to identity potential hazards stemming from neglecting or intentional errors of the human operators; and to prevent their consequences, which are measured in terms of quantified risk values [11]. However, these standardized safety control mechanisms study the issues unilateral focusing either on the robots' features or on the human operator behavioral regulations and, thus, in some cases fail to model or predict hazardous events that stem from the true collaboration. On the other hand, research endeavors in safety analysis realized powerful tools that enable mainly hardware-related fault forecasting, which aims at estimating the cause-consequence chain of fault occurrence [12]. Such methods can be either _Bottom-Up_ where a fault effect on the system is estimated in terms of cause-consequence, severity and probability, e.g., Failure Mode, Effects & Criticality Analysis (FMECA) [13], or _Top-Down_, where the determination of faults also induces identified unwanted effects, e.g., Fault Tree Analysis (FTA) [14], Hazard and Operability Analysis (HAZOP) [15]). Another recently introduced, yet powerful hazard analysis technique, namely System-Theoretic Process Analysis (STPA) [16], is based on the Systems Theoretic Accident Model and Process (STAMP) accident causation model and builds a model of the control structure of the system to identify control-related flaws. The main advantage of STPA, when compared to other hazard analysis methods, is that it forces a holistic study of the system to identify unsafe control actions that may lead to hazardous situations [17]. When it comes to HRC applications, with STPA, one can study simultaneously both the hardware as well as the human factor as a unified ecosystem that incorporates each other parameter and possible action related to any component of the examined system [18]. In this work, we introduce a novel method that studies the STPA as a decision making tool, based on a variation of the Markov belief propagation model, that associates un/safe control actions with un/safe states during collaborative tasks by incorporating the uncertainty of the observations obtained upon a selected action.