MIT AI Risk Repository · Risk Sub-Category · 59.25.05
(5) Monitoring and maintenance
Category: AI lifecycle stage
On this page 3 sections
Description
"Conclusively, the AI life cycle model terminates with the maintenance and monitoring stage, which aligns with the referenced models."
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
- Domain
- —
- Subdomain
- —
- Causal entity
- Not coded
- Intent
- Not coded
- Timing
- Post-deployment
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