A manager wants to ensure that only quality data is used in their AI model. Which scenario is most likely to lead to an unfair and biased outcome?
A.
A financial institution builds a fraud detection model using transaction data that is incomplete, lacking details about the merchants involved in a significant percentage of the transactions.
B.
A company ' s customer service natural language processing model is trained on a dataset containing incorrectly encoded text from older website versions.
C.
A marketing team creates a personalized recommendation engine based on website clickstream data that includes bot traffic.
D.
A technology firm trains a facial recognition system primarily using images of individuals from one demographic group, with limited representation from other groups.
Training a facial-recognition system predominantly on one demographic creates representation bias. The model receives insufficient examples from other groups and will probably perform less accurately for those populations, producing systematically unequal outcomes. This is directly associated with fairness because model performance varies according to demographic characteristics. The other scenarios describe serious data-quality problems, but their primary effects differ. Missing merchant details can reduce fraud-detection accuracy, incorrectly encoded text introduces corruption, and bot traffic distorts customer-behavior signals. Those defects may degrade overall performance without necessarily disadvantaging a protected or underrepresented group. Responsible AI development requires representative datasets, subgroup-level evaluation, documented data provenance, and ongoing monitoring for unequal error rates. Therefore, the facial-recognition dataset presents the clearest and most direct risk of an unfair and biased outcome.
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