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.

人工智能领域的最新进展建立了解决具有挑战性任务的基础，然而，与人工智能的集成带来了新的风险，因此为了从其优势中受益，必须充分应对与人工智能相关的风险。本文介绍了 AI 危害管理（AIHM）框架，提供了一个结构化的过程，以系统地识别、评估和处理 AI 危害。建议的过程与开发同时进行，以确保任何 AI 危害都能在 AI 系统生命周期的最早阶段捕捉到。此外，为了确保 AI 系统的可审计性，提出的框架系统地记录证据，表明已经减少了识别出的 AI 危害的潜在影响到可容忍水平的程度。该框架基于来自综合的最新分析的 AI 危害列表，并提供了一个支持识别出的 AI 危害的最佳处理方式的分类方法。此外，我们举例说明了 AIHM 框架如何通过系统地降低识别的危害对电网 AI 用例的总体质量产生积极影响。