arXiv:2606.29623v1 Announce Type: cross Abstract: Rare events govern the safety profile of modern AI systems, yet their probabilities are extremely difficult to estimate: direct Monte Carlo requires prohibitive sample budgets. Subset Simulation (SS) addresses this by decomposing a rare-event probability into moderate conditional probabilities over nested intermediate events. However, classical SS requires a handcrafted scalar performance function whose sublevel sets define those events, demanding detailed knowledge of the failure geometry and limiting transfer to new domains. We propose SCARCE
The increasing complexity and deployment of AI systems necessitate robust methods for safety validation, especially concerning rare but critical failure events that are difficult to predict.
This development allows for more accurate and efficient characterization of rare-event probabilities in AI, directly impacting the safety, reliability, and regulatory acceptance of advanced AI systems.
The ability to analyze rare events without handcrafting performance functions removes a significant bottleneck in AI safety analysis, broadening the applicability of such techniques to more diverse and complex AI domains.
- · AI developers
- · AI safety researchers
- · High-stakes AI applications (e.g., autonomous vehicles, medical AI)
- · Regulatory bodies
- · AI systems with unaddressed rare-event risks
- · Methods reliant on extensive manual feature engineering for safety analysis
SCARCE facilitates a more systematic and less labor-intensive approach to identifying and quantifying rare failure modes in AI systems.
Improved rare-event characterization will accelerate the deployment of safer and more reliable AI in critical infrastructure and public-facing applications.
The reduced barrier to rare-event analysis could lead to new industry standards for AI safety and a competitive advantage for companies adopting these advanced techniques early.
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Read at arXiv cs.LG