A hiring-screening model consistently ranks candidates from one demographic group lower. Which responsible-AI dimension does this violate, and what's the first step to address it?
A.Explainability — make the model's reasoning transparent
Explainability helps diagnose but the problem itself is fairness.
B.Fairness — evaluate training data for demographic bias
Correct. Demographic disparities are a fairness issue; audit the data first.
C.Privacy — encrypt the candidate data
Privacy protects data confidentiality, not equitable outcomes.
D.Robustness — test against adversarial inputs
Robustness concerns adversarial/noisy inputs, not systematic group bias.
Why this answer
Systematic disadvantage to a demographic group is a fairness/bias problem. The first step is auditing the training data and model outputs for bias — then mitigating (rebalancing data, bias metrics, human review).
Correct answer: B · Last reviewed 2026-10-26
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