提出AI风险的多维度量化框架，助力企业定制化风险管理。
An Artificial Intelligence Value at Risk Approach: Metrics and Models
- 构建涵盖数据、公平、准确等维度的AI风险度量体系。
- 融合FAIR模型与机器学习，实现风险场景的可量化建模。
- 适合金融、合规及技术团队协作推进AI风险治理。
人工智能风险具有多维特性，同一风险情景可能同时涉及法律、运营和财务维度。随着新的人工智能监管出台，当前人工智能风险管理的技术水平仍显不足。尽管已有多种方法和通用标准，但真正具备实施价值的指南仍十分罕见，核心问题在于需为具体AI风险场景定制风险指标与模型。目前财务、法务及政府合规团队对AI系统的技术细节普遍缺乏了解，数据科学家与AI工程师则成为最合适的实施者。必须将人工智能风险分解为数据保护、公平性、准确性、鲁棒性和信息安全等多个维度。因此，关键任务是开发能降低决策不确定性的度量指标与风险模型，以支持对AI系统风险的有效管理。本文旨在为AI利益相关方提供风险管理的深度指导，虽不极端技术化，但要求具备风险评估、不确定性量化、FAIR模型、机器学习、大语言模型及AI上下文工程的基础知识。文中示例力求简洁易懂，可扩展至特定定制化环境。本文呈现了人工智能风险相互依赖关系的全景图，并展示如何在风险场景中整合建模，以实现系统性风险管理。
原文摘要 · Abstract (English)
Artificial intelligence risks are multidimensional in nature, as the same risk scenarios may have legal, operational, and financial risk dimensions. With the emergence of new AI regulations, the state of the art of artificial intelligence risk management seems to be highly immature due to upcoming AI regulations. Despite the appearance of several methodologies and generic criteria, it is rare to find guidelines with real implementation value, considering that the most important issue is customizing artificial intelligence risk metrics and risk models for specific AI risk scenarios. Furthermore, the financial departments, legal departments and Government Risk Compliance teams seem to remain unaware of many technical aspects of AI systems, in which data scientists and AI engineers emerge as the most appropriate implementers. It is crucial to decompose the problem of artificial intelligence risk in several dimensions: data protection, fairness, accuracy, robustness, and information security. Consequently, the main task is developing adequate metrics and risk models that manage to reduce uncertainty for decision-making in order to take informed decisions concerning the risk management of AI systems. The purpose of this paper is to orientate AI stakeholders about the depths of AI risk management. Although it is not extremely technical, it requires a basic knowledge of risk management, quantifying uncertainty, the FAIR model, machine learning, large language models and AI context engineering. The examples presented pretend to be very basic and understandable, providing simple ideas that can be developed regarding specific AI customized environments. There are many issues to solve in AI risk management, and this paper will present a holistic overview of the inter-dependencies of AI risks, and how to model them together, within risk scenarios.
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