  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.
