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Generative AI Leader Practice Question: Developing an AI-powered interview assistant that…
A company is developing an AI-powered interview assistant that screens job applicants. The responsible AI team wants to ensure the model does not discriminate based on gender, race, or age. Which TWO practices should they implement?
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
✓
Regularly evaluate the model's outputs for bias using intersectional test sets.
Option B is correct because regularly evaluating the model's outputs for bias using intersectional test sets allows the team to detect discrimination across combinations of protected attributes such as gender, race, and age, which is essential for responsible AI screening tools. Option E is correct because a diverse and representative training dataset that includes candidates from various demographics helps reduce sampling bias and enables the model to learn patterns that generalize fairly across groups. Option A is not appropriate because deploying without human oversight removes the ability to catch discriminatory or erroneous decisions, and consistency alone does not guarantee fairness. Option C is unrelated because SynthID watermarking is used to mark AI-generated content, not to mitigate bias in hiring models. Option D is not sufficient and can be harmful because simply removing demographic attributes does not ensure fairness and may allow proxy variables to perpetuate discrimination while also preventing the measurement of disparate impact.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy the model without human oversight to ensure consistency.
Why it's wrong here
Removing human oversight does not prevent bias; it removes the mechanism that catches discriminatory outputs before they affect candidates. It is tempting because consistency sounds objective, but consistent bias is still bias, and fairness auditing requires human review of model decisions.
- ✓
Regularly evaluate the model's outputs for bias using intersectional test sets.
Why this is correct
Intersectional test sets expose compounded bias across overlapping attributes such as race and gender, which single-axis evaluations miss. This directly satisfies the stem's requirement to prevent discrimination on gender, race, and age, since a model can pass isolated checks yet still disadvantage, for example, older women.
- ✗
Use SynthID watermarking on all model outputs.
Why it's wrong here
SynthID watermarking marks AI-generated content for provenance; it does nothing to detect or mitigate gender, race or age discrimination in screening decisions. It is tempting because it is a genuine responsible-AI tool, but it addresses content authenticity, not bias in candidate evaluation.
- ✗
Remove all demographic attributes from the training data to ensure fairness.
Why it's wrong here
Removing demographic attributes does not ensure fairness, since correlated proxies such as postcode, name or school still encode those characteristics. It is tempting because blindness feels neutral, but bias auditing and fairness metrics on protected groups require those attributes to be retained for measurement.
- ✓
Use a diverse and representative training dataset that includes candidates from various demographics.
Why this is correct
A diverse, representative training dataset reduces the historical under-representation that causes models to score certain demographic groups unfairly. This addresses the root cause of discriminatory screening by ensuring the model learns patterns reflecting all applicant groups.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This Generative AI Leader practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Generative AI Leader exam.