MIT AI Risk Repository · Risk Category · 59.25.00
AI lifecycle stage
Description
"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 prepar
From AI Hazard Management: A Framework for the Systematic Management of Root Causes for AI Risks (Schnitzer2024), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
Other entries from Schnitzer2024
- Inadequate specification of ODD
- Inappropriate degree of automation
- Inadequate planning of performance requirements
- Insufficient AI development documentation
- Inappropriate degree of transparency to end users
- Missing requirements for the implemented hardware
- Choice of untrustworthy data source
- Lack of data understanding
- Discriminative data bias
- Harming users’ data privacy
- Incorrect data labels
- Data poisoning