MIT AI Risk Repository · Risk Sub-Category · 59.27.02
AI system
Category: Level
Description
"For instance, the main person responsible for an AI hazard manifesting on the AI system level would be the AI developer, whereas an AI hazard affecting the whole AI application requires a more diverse group, including domain experts."
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