The potential for rare, extreme outcomes in AI behavior or decision-making that fall outside normal expectations and require special mitigation planning.
Focuses on low-probability but high-impact events - catastrophic model failures, adversarial exploits, or emergent behavior in complex systems. Tail-risk governance includes stress-testing models on extreme scenarios, defining contingency plans (kill switches, fail-safe modes), and maintaining reserves (financial, operational) to absorb shocks. Risk metrics (e.g., Value at Risk) are extended to cover AI-driven decision contexts.
An autonomous-drone inspection service simulates rare failure modes - GPS spoofing, extreme weather - and verifies that the system safely lands rather than continues unstable flight. The company’s tail-risk plan includes emergency-grounding procedures and redundant communication links to minimize potential harm.
