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Generative AI Leader Practice Question: A software company wants to provide users with a…
A software company wants to provide users with a clear understanding of when and why their AI system may produce incorrect answers. Which tool from the Responsible AI toolkit should they use to communicate model limitations?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Model Cards
Model Cards are designed to communicate model performance, intended use, and limitations to stakeholders in a standardized format.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
People + AI Guidebook
Why it's wrong here
The People + AI Guidebook offers human-centred design guidance for building AI products, not a mechanism for disclosing a deployed model's specific limitations to its users. It appeals because it addresses user understanding broadly, but it prescribes design processes rather than communicating where and why a particular system errs.
- ✗
PAIR Explorables
Why it's wrong here
PAIR Explorables are interactive visual demonstrations that illustrate AI concepts and trade-offs for general audiences, not artefacts documenting a specific system's failure modes. They attract teams seeking user-facing transparency, yet communicating concrete model limitations requires documentation tied to the deployed system rather than conceptual explainers.
- ✓
Model Cards
Why this is correct
Model Cards document a model's intended use, performance metrics, and known limitations, directly satisfying the requirement to communicate when and why incorrect answers occur. Unlike dashboards or error-analysis tools, they are static disclosure artefacts designed for stakeholders, making them the appropriate Responsible AI toolkit component for transparently explaining limitations to users.
- ✗
Datasheets for Datasets
Why it's wrong here
Datasheets for Datasets document the composition, collection and intended use of training data, not a model's runtime failure modes. They tempt teams pursuing transparency because they expose provenance, yet they describe datasets rather than communicating to end users when and why the AI system produces incorrect answers.
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