Recent advancements in the field of Artificial Intelligence (AI) establish the basis to address challenging tasks. However, with the integration of AI, new risks arise. Therefore, to benefit from its advantages, it is essential to adequately handle the risks associated with AI. Existing risk management processes in related fields, such as software systems, need to sufficiently consider the specifics of AI. A key challenge is to systematically and transparently identify and address AI risks' root causes - also called AI hazards. This paper introduces the AI Hazard Management (AIHM) framework, which provides a structured process to systematically identify, assess, and treat AI hazards. The proposed process is conducted in parallel with the development to ensure that any AI hazard is captured at the earliest possible stage of the AI system's life cycle. In addition, to ensure the AI system's auditability, the proposed framework systematically documents evidence that the potential impact of identified AI hazards could be reduced to a tolerable level. The framework builds upon an AI hazard list from a comprehensive state-of-the-art analysis. Also, we provide a taxonomy that supports the optimal treatment of the identified AI hazards. Additionally, we illustrate how the AIHM framework can increase the overall quality of a power grid AI use case by systematically reducing the impact of identified hazards to an acceptable level.

翻译：人工智能领域的最新进展为解决复杂任务奠定了基础。然而，随着AI的整合应用，新的风险也随之涌现。因此，要充分利用其优势，必须妥善处理与AI相关的风险。现有软件系统等相关领域的风险管理流程，未能充分考量AI的特殊性。关键挑战在于如何系统化、透明化地识别和处理AI风险的深层根源（即AI危害）。本文提出AI危害管理（AIHM）框架，该框架通过结构化流程系统性地识别、评估和处理AI危害。所提出的流程与开发过程并行推进，确保在AI系统生命周期的最早阶段捕获任何AI危害。此外，为确保AI系统的可审计性，该框架系统性地记录证据，证明已识别AI危害的潜在影响可降至可接受水平。该框架基于对最新技术现状的全面分析构建了AI危害清单，同时提供支持优化处理已识别AI危害的分类体系。最后，我们通过电网AI用例验证AIHM框架如何通过系统性降低已识别危害的影响至可接受水平，从而提升系统整体质量。