Risk-Based Decision-Making Fallacies: Why Present Functional Safety Standards are Not Enough

Functional safety of a system is the part of its overall safety that depends on the system operating correctly in response to its inputs. Safety is defined as the absence of unacceptable/unreasonable risk by functional safety standards, which enforce safety requirements in each phase of the development process of safety-critical software and hardware systems. Acceptability of risks is judged within a framework of analysis with contextual and cultural aspects by individuals who may introduce subjectivity and misconceptions in the assessment. While functional safety standards elaborate much on the avoidance of unreasonable risk in the development of safety-critical software and hardware systems, little is addressed on the issue of avoiding unreasonable judgments of risk. Through the studies of common fallacies in risk perception and ethics, we present a moral-psychological analysis of functional safety standards and propose plausible improvements of the involved risk-related decision making processes, with a focus on the notion of an acceptable residual risk. As a functional safety reference model, we use the functional safety standard ISO 26262, which addresses potential hazards caused by malfunctions of software and hardware systems within road vehicles and defines safety measures that are required to achieve an acceptable level of safety. The analysis points out the critical importance of a robust safety culture with developed countermeasures to the common fallacies in risk perception, which are not addressed by contemporary functional safety standards. We argue that functional safety standards should be complemented with the analysis of potential hazards caused by fallacies in risk perception, their countermeasures, and the requirement that residual risks must be explicated, motivated, and accompanied by a plan for their continuous reduction. This approach becomes especially important in contemporary developed autonomous vehicles with increasing computational control by increasingly intelligent software applications.