Companies like OpenAI, Google DeepMind, and Anthropic have the stated goal of building artificial general intelligence (AGI) - AI systems that perform as well as or better than humans on a wide variety of cognitive tasks. However, there are increasing concerns that AGI would pose catastrophic risks. In light of this, AGI companies need to drastically improve their risk management practices. To support such efforts, this paper reviews popular risk assessment techniques from other safety-critical industries and suggests ways in which AGI companies could use them to assess catastrophic risks from AI. The paper discusses three risk identification techniques (scenario analysis, fishbone method, and risk typologies and taxonomies), five risk analysis techniques (causal mapping, Delphi technique, cross-impact analysis, bow tie analysis, and system-theoretic process analysis), and two risk evaluation techniques (checklists and risk matrices). For each of them, the paper explains how they work, suggests ways in which AGI companies could use them, discusses their benefits and limitations, and makes recommendations. Finally, the paper discusses when to conduct risk assessments, when to use which technique, and how to use any of them. The reviewed techniques will be obvious to risk management professionals in other industries. And they will not be sufficient to assess catastrophic risks from AI. However, AGI companies should not skip the straightforward step of reviewing best practices from other industries.

翻译：OpenAI、谷歌 DeepMind 和 Anthropic 等企业公开宣称其目标是构建通用人工智能（AGI）——即在多种认知任务上达到或超越人类水平的 AI 系统。然而，各界日益担忧 AGI 可能带来灾难性风险。有鉴于此，AGI 企业亟需大幅提升其风险管理实践。为支持此类努力，本文回顾了其他安全关键行业中流行的风险评估技术，并提出 AGI 企业可借鉴这些技术评估 AI 灾难性风险的途径。文章讨论了三种风险识别技术（情景分析、鱼骨图法、风险类型学与分类法）、五种风险分析方法（因果映射、德尔菲法、交叉影响分析、领结分析、系统理论过程分析）以及两种风险评价技术（检查表与风险矩阵）。针对每种技术，文章阐释其工作原理，提出 AGI 企业适用的实施方式，剖析其优势与局限，并提出建议。最后，文章探讨了风险评估的开展时机、技术选择策略及具体使用方法。这些技术对其他行业的风险管理专业人士而言显而易见，且不足以完全应对 AI 灾难性风险。然而，AGI 企业不应跳过借鉴其他行业最佳实践这一基础步骤。