An AI governance team is conducting a risk assessment of an AI system that scores job applicants for a technology company. Historical hiring data used for training reflects a decade of predominantly male hiring in technical roles. Which risk does this create beyond ordinary model error?
Select an answer to reveal the explanation.
Short Explanation
Here's the deal — a is correct because training a hiring model on historically biased data encodes those biases — the model learns that male candidates have been successful historically and penalizes female candidates, amplifying and perpetuating past discrimination at scale and speed. B is a technical modeling error unrelated to historical bias encoding.
Full Explanation
A is correct because training a hiring model on historically biased data encodes those biases — the model learns that male candidates have been successful historically and penalizes female candidates, amplifying and perpetuating past discrimination at scale and speed. B is a technical modeling error unrelated to historical bias encoding. C requires evidence of deliberate data manipulation. D is a temporal generalization risk, not the primary bias concern.