Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
Which THREE strategies should be combined to effectively reduce biased outputs in a generative AI model? (Choose three.)
⚠ Common exam trap
Google often tests the misconception that increasing randomness (temperature) or training on biased data can somehow reduce bias, when in fact both actions worsen the problem by either amplifying noise or embedding the bias deeper into the model's weights.
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
✓
Implement safety filters targeting hate speech and stereotypes.
Implementing safety filters targeting hate speech and stereotypes directly blocks the generation of biased or harmful content at the output layer. These filters use predefined rule sets or trained classifiers to detect and suppress language that reflects demographic or cultural biases, reducing the risk of the model producing offensive or stereotypical responses.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement safety filters targeting hate speech and stereotypes.
Why this is correct
Safety filters block explicitly biased content.
- ✓
Conduct human evaluation and feedback loops.
Why this is correct
Human review identifies subtle biases and improves the model iteratively.
- ✓
Use diverse few-shot examples that represent different demographics.
Why this is correct
Diverse examples guide the model toward fairer outputs.
- ✗
Raise the temperature to increase output variability.
Why it's wrong here
Higher temperature can increase unpredictable biased statements.
- ✗
Fine-tune the model on a biased dataset to learn patterns.
Why it's wrong here
Fine-tuning on biased data entrenches those biases.
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