arXiv:2607.01840v1 Announce Type: new Abstract: Fault trees are a widely used as effective risk models for complex systems, answering the question "what can go wrong?", especially through minimal cut set analysis. We study fault trees from the perspective of Halpern & Pearl's theory of actual causality. This allows us to use fault trees to answer the question "why has it gone wrong?", which is fundamental to failure diagnostics. We give a complete classification of each of the different notions of actual causality in terms of the fault tree's graph structure and logical structure, and show how
The increasing complexity of AI systems and critical infrastructure necessitates more sophisticated methods for failure diagnostics and causal analysis.
This research provides a fundamental approach to understanding 'why' failures occur in complex systems, which is critical for enhancing reliability, safety, and diagnostics across various high-stakes applications.
The application of actual causality theory to fault trees enhances their utility beyond 'what can go wrong' to 'why it went wrong', offering a more robust framework for failure analysis in AI and other complex systems.
- · AI system developers
- · Safety engineers
- · Risk management professionals
- · Complex systems operators
- · Systems with opaque failure modes
- · Reactive diagnostics approaches
Improved diagnostics and reduced downtime in AI-driven and critical infrastructure systems.
Increased trust and adoption of advanced autonomous systems due to a better understanding of their failure mechanisms.
The development of 'explainable AI' for system failures, leading to new regulatory and audit frameworks based on causal reasoning.
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Read at arXiv cs.AI