AIGP Question of the Day — 4 August 2026
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A bank's loan model produces skewed refusals for applicants from certain neighbourhoods even though race was excluded from the training data. Postcode was retained as a feature. What best explains the outcome?
- Automation bias among the loan officers who review and act on the model's outputs
- Proxy discrimination operating through the retained postcode feature
- Concept drift in the relationship between applicant features and repayment behaviour
- Underfitting, because removing the race feature left the model too little signal
Answer: Proxy discrimination operating through the retained postcode feature
Postcode is highly correlated with the excluded protected attribute, so the model reconstructs the discriminatory pattern through that proxy (b). (a) describes human over-reliance on model outputs and does not explain the model's own skew. (c) refers to a relationship changing over time, whereas this pattern was present from training. (d) misapplies underfitting, which is a general failure to capture signal rather than a group-specific disparity.
Postcode is highly correlated with the excluded protected attribute, so the model reconstructs the discriminatory pattern through that proxy (b). (a) describes human over-reliance on model outputs and does not explain the model's own skew. (c) refers to a relationship changing over time, whereas this pattern was present from training. (d) misapplies underfitting, which is a general failure to capture signal rather than a group-specific disparity.
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