Common-Cause Failures in Physical AI: Estimating β-Coefficients for Redundant Safety Architectures

Physical AI systems commonly claim 'redundant' or 'dual-channel' safety architectures. Per IEC 61508-6 Annex D, the efficacy of redundancy depends on the β-coefficient: the fraction of channel failures that are common-cause. A redundancy claim without β disclosure is therefore unverifiable. This paper presents a methodology for estimating β from publicly available architecture information, applied to five anonymized Physical AI architectures and a wider survey of approximately 30 cases. Most claimed-redundant architectures show estimated β > 5%, with software-only configurations approaching 100% for operating-system-level common-cause failures. We provide a 12-question evaluator's checklist for safety engineers, due-diligence reviewers, regulators, and standards bodies. Supplementary material includes a β-estimation worksheet (xlsx) operationalising the five-step methodology.