hardMultiple Choice
Generative AI Leader Practice Question: An e-commerce company uses a generative AI model…
An e-commerce company uses a generative AI model to generate product descriptions. They observe that descriptions for high-end products use more sophisticated language compared to budget products, potentially reinforcing class stereotypes. What is the most likely cause, and what should they do to mitigate it?
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
✓
The training data reflects real-world associations; fine-tune with a balanced dataset that includes diverse product descriptions across price ranges
The bias stems from training data correlations. Fine-tuning on balanced, diverse data can reduce stereotypical associations. The other options either do not address the root cause or are less effective.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The training data reflects real-world associations; fine-tune with a balanced dataset that includes diverse product descriptions across price ranges
Why this is correct
Biased associations in the training corpus skew the model's language toward real-world price stereotypes. Fine-tuning on a balanced dataset containing diverse product descriptions across price ranges directly counteracts this, satisfying the stem's requirement to mitigate stereotype reinforcement at its source rather than masking outputs post-generation.
- ✗
The safety filters are too aggressive; relax them
Why it's wrong here
Aggressive safety filters suppress harmful or toxic output; they do not generate the sophisticated-versus-basic vocabulary split, so relaxing them removes guardrails without addressing the skew. Safety filters are tempting because they are the visible content control, and tuning them is correct when outputs are wrongly blocked as unsafe.
- ✗
The temperature parameter is set too low; increase it to introduce more randomness
Why it's wrong here
Low temperature makes sampling deterministic and consistent; it does not create the systematic high-end versus budget vocabulary disparity, and raising it adds randomness rather than removing stereotype. Temperature tuning is tempting as a quick creativity dial, and is correct when outputs are repetitive or need varied phrasing.
- ✗
The model architecture is biased; switch to a different base model
Why it's wrong here
The skew stems from biased training data and prompt or label associations, not the architecture itself; swapping base models leaves the underlying data imbalance intact. Replacing the model is tempting because it appears to reset behaviour, and would be right where a specific architecture demonstrably cannot meet capability requirements.
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