The ML/AI system provides outputs, predictions, or decisions without any accompanying disclaimer clarifying the potential limitations, uncertainties, or risks associated with the generated results. This absence might lead users to over-rely on the system's outputs without considering its inherent limitations or the context of its training data.
- Disclaimers: Always accompany ML/AI outputs with clear disclaimers that provide context for the generated results.
- User Education: Educate users on the potential risks and limitations associated with the AI/ML system's outputs.
- Human-in-the-Loop: In applications where the risk of misinterpretation is high, consider including a human-in-the-loop to validate and contextualize AI outputs.
- CWE-693: Protection Mechanism Failure (This is a broad category, but the idea of not having a protection mechanism, such as a disclaimer, fits this CWE in a conceptual manner.)
Absence of a disclaimer can lead to:
- Misunderstanding: Users might misunderstand the capabilities and limitations of the AI/ML system.
- Over-reliance: Users might over-rely on the system's outputs, potentially making critical decisions based solely on AI predictions.
- Liability: Organizations deploying the ML/AI system might face legal or reputational challenges if users feel they were misled by system outputs.