The answer is that the model may produce biased predictions against minorities, which is the most significant ethical risk. This occurs because training on historical data that underrepresents minority populations creates a model with insufficient examples to learn accurate patterns for those groups, leading to systematically less equitable or accurate outputs—a direct violation of fairness in AI ethics. On the Salesforce AI Associate exam, this scenario tests your understanding of how data representation gaps cause algorithmic bias, often appearing in healthcare or lending contexts where harm is most acute. A common trap is to focus on model accuracy overall rather than recognizing that fairness is compromised when subgroups are underrepresented. Remember the memory tip: “Missing data means missing fairness”—if the training data lacks diversity, the model’s predictions will be biased against the groups it barely sees.
AI Associate Ethical Considerations of AI Practice Question
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.
A healthcare provider uses an AI model to predict patient readmission risk. The model is trained on historical data that underrepresents minority populations. What is the MOST significant ethical risk?
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 produce biased predictions against minorities
Option C is correct because training on historical data that underrepresents minority populations leads to a model that has insufficient examples to learn patterns for those groups, resulting in biased predictions that systematically disadvantage minorities. This is a direct violation of fairness in AI ethics, as the model's outputs will be less accurate or equitable for underrepresented groups, potentially causing harm in critical healthcare decisions like readmission risk assessment.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 may overfit to the majority population
Why it's wrong here
Overfitting is a technical problem, not an ethical one.
✗
The model cannot scale to real-time predictions
Why it's wrong here
Scalability is a performance concern, not ethical.
✓
The model may produce biased predictions against minorities
Why this is correct
Underrepresentation in training data causes algorithmic bias, an ethical risk.
Related concept
Read the scenario before looking for a memorised answer.
✗
The model lacks explainability
Why it's wrong here
Explainability is relevant but secondary to bias in this scenario.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the distinction between a technical symptom (like overfitting) and the core ethical consequence (like biased predictions), so candidates may incorrectly choose overfitting as the most significant risk instead of recognizing that the ethical harm to minorities is the primary concern.
Trap categories for this question
Scenario analysis trap
Explainability is relevant but secondary to bias in this scenario.
Detailed technical explanation
How to think about this question
Under the hood, machine learning models minimize a loss function over the training data; when minority populations are underrepresented, the model prioritizes accuracy on the majority class, leading to higher error rates for minority groups. In healthcare, this can manifest as systematically underestimating readmission risk for minority patients, potentially denying them necessary follow-up care or resources. Real-world examples include models that fail to detect conditions like sepsis in minority patients due to skewed training data, highlighting how data imbalance directly causes ethical harm.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Ethical Considerations of AI — This question tests Ethical Considerations of AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The model may produce biased predictions against minorities — Option C is correct because training on historical data that underrepresents minority populations leads to a model that has insufficient examples to learn patterns for those groups, resulting in biased predictions that systematically disadvantage minorities. This is a direct violation of fairness in AI ethics, as the model's outputs will be less accurate or equitable for underrepresented groups, potentially causing harm in critical healthcare decisions like readmission risk assessment.
What should I do if I get this AI Associate question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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