AIF-C01 Practice Question: Developing an AI system to screen job applications

Answer analysis

Option-by-option breakdown

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

• ✓

  Historical bias

  Why this is correct

  Historical bias arises when training data reflects past human decisions that already disadvantaged a group. Because the dataset encodes prior hiring outcomes skewed by gender, the model learns and reproduces those patterns, making historical bias the most likely type present here.

• ✗

  Measurement bias

  Why it's wrong here

  Measurement bias arises when the features or labels used to train the model are systematically inaccurate or inconsistently recorded, such as a proxy variable standing in for the true target. Here the labels faithfully record past decisions; the defect is that those decisions encode historical gender discrimination, which is historical or label bias.

• ✗

  Representation bias

  Why it's wrong here

  Representation bias occurs when certain groups are underrepresented in the training data relative to the population, so the model learns poorly for them. This dataset contains historical hiring decisions; the labels themselves encode past discriminatory outcomes, which is historical bias. Representation bias would be the answer if, say, women comprised a tiny fraction of applicants sampled.

• ✗

  Aggregation bias

  Why it's wrong here

  Aggregation bias arises when one model is applied across distinct subgroups that need separate treatment, not when historical labels encode past prejudice. The stem's biased hiring decisions describe historical bias, which is what this option would fit if the dataset pooled dissimilar populations under a single model.

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