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AI Associate AI Fundamentals Practice Question

A financial services firm uses an AI model to approve small business loans. The model denies loans at a much higher rate for businesses owned by minorities, even when financial indicators are similar. What is the MOST likely cause?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Bias in training data

Bias in training data can cause the model to learn historical discrimination patterns. Overfitting would not cause systematic demographic differences. Hallucination is for generative AI. Low recall may indicate the model misses some positive cases but doesn't explain the demographic disparity.

Answer analysis

Option-by-option breakdown

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

  • Low recall for minority groups

    Why it's wrong here

    Low recall would mean missing some minority applicants who should be approved, but the issue is higher denial rates across the board.

  • Bias in training data

    Why this is correct

    If historical loan decisions were biased against minority-owned businesses, the model would learn and perpetuate that bias.

  • Hallucination in model predictions

    Why it's wrong here

    Hallucination refers to generative AI producing false information, not relevant to loan approval models.

  • Overfitting to historical data

    Why it's wrong here

    Overfitting might reduce generalization but doesn't inherently cause demographic bias unless the training data already contains bias.

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