MIT AI Risk Repository · Risk Category · 59.08.00
Lack of data understanding
Description
"The correct understanding of the used data for developing an AI system is a prerequisite to avoid data shortcomings and hinders the development of an AI system which is best suiting for the intended functionality."
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
- Subdomain
- 7.0
- Causal entity
- Human
- Intent
- Unintentional
- Timing
- Pre-deployment
How other frameworks describe this risk
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
- Discriminative data bias
- Harming users’ data privacy
- Incorrect data labels
- Data poisoning
- Insufficient data representation