Which is Better for AI Safety: STAMP/STPA or HAZOP/PHA?

STAMP/STPA and traditional PHA methods like HAZOP represent fundamentally different safety analysis philosophies. STAMP/STPA views accidents as control problems in complex socio-technical systems, focusing on hierarchical control structures and unsafe control actions that can occur even when all components function properly. 

In contrast, HAZOP operates on the principle that deviations from design intent cause accidents, using systematic guide words (No, More, Less, etc.) applied to process parameters to identify potential failure scenarios. Traditional PHA methods like FMEA and What-If analysis similarly focus on component failures and bottom-up analysis approaches.

Research demonstrates these methodologies are complementary rather than competitive. Studies show STPA identifies approximately 27% of hazards missed by HAZOP, while HAZOP finds about 30% of hazards that STPA overlooks. 

STAMP/STPA excels at analyzing software-intensive systems, complex organizational interactions, and novel technologies where traditional failure-based analysis falls short. 

HAZOP proves to be better for traditional process systems with well-defined physical parameters and established operational procedures, benefiting from decades of industrial experience and mature tooling.

For AI safety analysis, STAMP/STPA appears better suited to AI's systemic and emergent risks, but the choice becomes more nuanced when considering AI's integration into traditional process systems. 

While STPA naturally addresses algorithmic decision-making, human-AI interactions, and emergent behaviors that traditional failure analysis struggles with, AI increasingly operates within conventional industrial processes where HAZOP's systematic parameter analysis remains valuable. 

The real challenge lies in analyzing AI-augmented process control systems—where an AI controller making real-time decisions about flow rates or temperatures requires both STPA's systems perspective to understand the AI's control logic and HAZOP's structured approach to analyze how AI decisions affect physical process parameters. 

Rather than viewing these as competing methodologies, the most thoughtful approach recognizes that AI safety analysis may require STPA for understanding the AI system itself, while leveraging HAZOP's proven framework for analyzing how AI decisions propagate through traditional process systems—a hybrid necessity as AI becomes embedded throughout industrial infrastructure.