Evaluating AI safety requires statistically rigorous methods and risk metrics for understanding how the use of AI affects aggregated risk. However, much AI safety literature focuses upon risks arising from AI models in isolation, lacking consideration of how modular use of AI affects risk distribution of workflow components or overall risk metrics. There is also a lack of statistical grounding enabling sensitisation of risk models in the presence of absence of AI to estimate causal contributions of AI. This is in part due to the dearth of AI impact data upon which to fit distributions. In this work, we address these gaps in two ways. First, we demonstrate how scenario modelling (grounded in established statistical techniques such as Markov chains, copulas and Monte Carlo simulation) can be used to model AI risk holistically. Second, we show how lookalike distributions from phenomena analogous to AI can be used to estimate AI impacts in the absence of directly observable data. We demonstrate the utility of our methods for benchmarking cumulative AI risk via risk analysis of a logistic scenario simulations.

本研究解决了AI安全评估中缺乏综合性风险度量和模型的不足，尤其是AI对工作流程组件的风险分布的影响。研究提出使用情景建模和类比分布的创新方法来全面评估AI风险，并展示了其在基准累计AI风险分析中的有效性。这一方法有望为未来的AI安全评估提供更为可靠的统计基础。 

多变量人工智能风险的统计情景建模与类比分布