This AI Associate practice question tests your understanding of ethical considerations of ai. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Evaluating model performance across different groups.
Why wrong: Evaluation helps detect bias, so it reduces risk.
Excluding gender from the model features.
Why wrong: Excluding protected attributes reduces ethical risk.
Documenting model limitations and assumptions.
Why wrong: Documentation reduces risk by promoting transparency.
Including gender to improve model accuracy.
Using protected attributes can lead to biased outcomes.
Answer choices
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.
Option D is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Salesforce often tests the misconception that including more features always improves model performance, without considering the ethical implications of using protected attributes like gender.
Detailed technical explanation
Under the hood, including a protected attribute like gender can cause the model to learn spurious correlations (e.g., gender and performance rating) that may not generalize or may encode societal biases. In real-world scenarios, even if gender is not explicitly used, correlated features (e.g., department or tenure) can act as proxies, leading to indirect discrimination. Techniques like adversarial debiasing or reweighting are often needed to mitigate such risks while preserving accuracy.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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What to study next
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FAQ
Ethical Considerations of AI — This question tests Ethical Considerations of AI — Read the scenario before looking for a memorised answer..
The correct answer is: Including gender to improve model accuracy. — Option D is correct because 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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