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 and Infographic
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 below image
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.