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Ethical AI and Data PrivacyhardMultiple ChoiceObjective-mapped

AI Associate Ethical AI and Data Privacy Practice Question

An AI model predicts loan approvals, and the bank notices that the model disproportionately denies loans to a certain demographic group. Which combination of actions addresses the AI bias according to Salesforce's Trusted AI principles?

⚠ Common exam trap

A common mix-up: candidates think removing demographic data (Option C) is sufficient to eliminate bias, but they overlook that proxy variables can perpetuate discrimination, and Salesforce's principles require proactive auditing and transparency, not just data sanitization.

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

Audit the model for bias, provide transparency on decision factors, and require human review for denied applications

It aligns with Salesforce's Trusted AI principles, which emphasize accountability, transparency, and human oversight. Auditing the model for bias identifies disparities, providing transparency on decision factors ensures stakeholders understand how outcomes are determined, and requiring human review for denied applications introduces a safeguard against automated discrimination. This combination addresses bias without abandoning AI's benefits.

Answer analysis

Option-by-option breakdown

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

  • Disable the AI model and make all decisions manually

    Why it's wrong here

    Overly drastic; bias can be mitigated with proper controls.

  • Audit the model for bias, provide transparency on decision factors, and require human review for denied applications

    Why this is correct

    This approach aligns with accuracy, transparency, and empathy principles.

  • Remove demographic data from the model entirely and continue using it

    Why it's wrong here

    Removing demographic data does not guarantee fairness; bias can still exist through proxy variables.

  • Retrain the model with more data from the affected group and deploy automatically

    Why it's wrong here

    Simply retraining may not solve bias; oversight and transparency are also needed.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This AI Associate practice question is part of Courseiva's free Salesforce certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI Associate exam.