Monte Carlo Probabilistic Assessment of Loss of Human Control Over Advanced AI Using Bowtie Analysis

A quantitative risk assessment of human loss of control over advanced AI used a Bowtie diagram extended with fault tree and event tree analysis. Six primary threats were identified (recursive self‑improvement, power seeking, deceptive alignment, loss of corrigibility, off‑switch subversion, malicious misuse) and six consequences (systemic infrastructure collapse, economic breakdown, resource shortages, non‑human value lock‑in, human marginalization, global supply cascade failures). Preventive and mitigative barriers were assigned per pathway from expert literature. Input probabilities (threat base rates and barrier failure‑on‑demand values) were sourced from experts and modeled with triangular uncertainty distributions. A 1,000‑iteration Monte Carlo simulation propagated epistemic uncertainty, yielding a median probability of the top event (loss of human control) of 12.8% (90% CI: 11.3%–14.4%), roughly 1 in 8. The distribution is approximately symmetric with slight positive skew, indicating modest tail risk if barrier failures interact. Conditional on the top event, Expected Severity is 1.85 on a 1–10 scale (90% CI: 1.75–1.96), suggesting mitigation is effective in most scenarios. Results align with expert estimates and demonstrate barrier effects; narrow CIs reflect model consistency. Remaining tail risks support precautionary governance, increased alignment research, iterative risk modeling, and investment in international coordination with robust safety measures to reduce the existential risk of AI loss of control.