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Ethical AI and Data PrivacymediumMultiple ChoiceObjective-mapped

AI Associate Ethical AI and Data Privacy Practice Question

A sales team is using Einstein Lead Scoring and notices that leads from a certain geographic region are consistently scored lower, even when the lead's profile matches high-performing customers from other regions. What is the most likely cause and recommended first step?

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

The model may be biased due to underrepresented data from that region in the training set. Audit the model for bias and review training data demographics.

The low scores for a specific region likely indicate bias in the model due to historical data imbalances. The first step should be to audit the model for bias using tools like Fairness in AI or reviewing training data distribution.

Answer analysis

Option-by-option breakdown

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

  • The model is accurate; the region is genuinely underperforming. The team should accept the scores.

    Why it's wrong here

    Accepting without investigation could perpetuate bias, especially if historical data was skewed.

  • The model may be biased due to underrepresented data from that region in the training set. Audit the model for bias and review training data demographics.

    Why this is correct

    Bias auditing is the correct first step to identify if the model is unfairly penalizing leads based on geography.

  • The region has lower quality leads, so the scores are correct. The team should focus on other regions.

    Why it's wrong here

    Assuming correctness without analysis risks reinforcing biases.

  • Retrain the model with more data from that region, even if it means duplicating records.

    Why it's wrong here

    Duplicating records introduces artificial bias; proper data collection or resampling techniques should be used.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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