AIF-C01 Guidelines for Responsible AI Practice Question

Which TWO practices help ensure transparency in AI systems? (Choose 2)

⚠ Common exam trap

The AIF-C01 exam often tests the misconception that transparency means simplifying the model (e.g., removing features) or hiding logic (e.g., using ensembles or black-box models), when in fact transparency is achieved through explainability tools and thorough documentation of limitations and data sources.

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

✓

Use model-agnostic explainability tools like SHAP

Model-agnostic explainability tools like SHAP (SHapley Additive exPlanations) provide post-hoc explanations for any machine learning model by computing feature contributions based on cooperative game theory. This allows stakeholders to understand how each input feature influences a prediction, directly supporting transparency without requiring access to the model's internal structure.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Combine multiple models to obscure decision logic
Why it's wrong here
Obscuring logic is opposite of transparency.
✓
Use model-agnostic explainability tools like SHAP
Why this is correct
Explainability tools clarify decisions.
✗
Remove all features except the most predictive ones
Why it's wrong here
Removing features may reduce explainability.
✓
Provide documentation on model limitations and data sources
Why this is correct
Documentation supports transparency.
✗
Use black-box models to protect proprietary algorithms
Why it's wrong here
Black-box models reduce transparency.

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AIF-C01 Guidelines for Responsible AI Practice Question

Why each option matters

Option-by-option breakdown

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