AI lifecycle stage

"The first axis pertains to the life cycle of the AI system, as AI hazards may materialize during various phases of an AI system’s life cycle. For instance, issues triggered by bias in training data emerge during the data collection and preparation stages. On the other hand, data drift serves as an example of an AI hazard that arises during the AI system’s operation. Additionally, certain AI hazards may span multiple phases of the AI system, such as ”lack of data understanding”. This is because a proper understanding of the data by the AI developer is required in the data collection and preparation stage but also in the modeling stage."