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Ethical Considerations of AImediumMultiple ChoiceObjective-mapped

AI Associate Ethical Considerations of AI Practice Question

An AI Associate is asked to build a model that predicts employee performance. The dataset includes gender, department, and tenure. Which practice could introduce ethical risk?

⚠ Common exam trap

Salesforce often tests the misconception that including more features always improves model performance, without considering the ethical implications of using protected attributes like gender.

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

Including gender to improve model accuracy.

Including gender as a feature in a predictive model for employee performance can introduce bias and lead to unfair or discriminatory outcomes. Even if the model's accuracy improves, using protected attributes like gender may violate ethical guidelines and regulations such as GDPR or anti-discrimination laws, as it could perpetuate historical biases or result in disparate impact.

Answer analysis

Option-by-option breakdown

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

  • Evaluating model performance across different groups.

    Why it's wrong here

    Evaluation helps detect bias, so it reduces risk.

  • Excluding gender from the model features.

    Why it's wrong here

    Excluding protected attributes reduces ethical risk.

  • Documenting model limitations and assumptions.

    Why it's wrong here

    Documentation reduces risk by promoting transparency.

  • Including gender to improve model accuracy.

    Why this is correct

    Using protected attributes can lead to biased outcomes.

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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.