- A
Remove social media data from the model immediately.
Why wrong: Removing it without analysis may discard a valid predictor, but more importantly, it does not address the ethical question of relevance.
- B
Increase the weight of traditional factors like income and payment history.
Why wrong: Merely adjusting weights does not justify the inclusion of social media data if it is biased.
- C
Conduct a thorough analysis to determine whether social media activity is a legitimate, non-discriminatory predictor of creditworthiness.
Validating the factor's relevance and fairness ensures the model is both ethical and effective.
- D
Continue using the current model but offer an alternative traditional scoring option.
Why wrong: Offering alternatives does not resolve the bias in the main model; it may still be considered discriminatory.
Fairness in Credit Scoring with Social Media Data
This AI Associate practice question tests your understanding of ethical considerations of ai. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 credit scoring company develops an AI model that includes social media activity as a factor. The model awards higher scores to individuals with many online connections and consistent posting. Consumer advocates argue that this penalizes individuals with limited internet access or those who value privacy. The company defends the model, stating that it predicts creditworthiness better than traditional models. However, a regulatory body is investigating potential discrimination. The company wants to address ethical concerns without completely abandoning the model. Which approach is most appropriate?
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
Conduct a thorough analysis to determine whether social media activity is a legitimate, non-discriminatory predictor of creditworthiness.
Option C is correct because conducting a thorough analysis to validate the relevance and fairness of social media data ensures that the factor is both predictive and non-discriminatory, addressing ethical concerns without prematurely abandoning the model. Option A removes the data without evidence of harm, potentially discarding a legitimate predictor. Option B increases weight on traditional factors but does not resolve the core issue of whether social media data is discriminatory. Option D offers an alternative but does not address the ethical concerns inherent in the original model.
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.
- ✗
Remove social media data from the model immediately.
Why it's wrong here
Removing it without analysis may discard a valid predictor, but more importantly, it does not address the ethical question of relevance.
- ✗
Increase the weight of traditional factors like income and payment history.
Why it's wrong here
Merely adjusting weights does not justify the inclusion of social media data if it is biased.
- ✓
Conduct a thorough analysis to determine whether social media activity is a legitimate, non-discriminatory predictor of creditworthiness.
Why this is correct
Validating the factor's relevance and fairness ensures the model is both ethical and effective.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Continue using the current model but offer an alternative traditional scoring option.
Why it's wrong here
Offering alternatives does not resolve the bias in the main model; it may still be considered discriminatory.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 practitioner preparing for the AI Associate exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
Visual reference
What to study next
Got this wrong? Here's your next step.
Identify which AI Associate exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this AI Associate question test?
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: Conduct a thorough analysis to determine whether social media activity is a legitimate, non-discriminatory predictor of creditworthiness. — Option C is correct because conducting a thorough analysis to validate the relevance and fairness of social media data ensures that the factor is both predictive and non-discriminatory, addressing ethical concerns without prematurely abandoning the model. Option A removes the data without evidence of harm, potentially discarding a legitimate predictor. Option B increases weight on traditional factors but does not resolve the core issue of whether social media data is discriminatory. Option D offers an alternative but does not address the ethical concerns inherent in the original model.
What should I do if I get this AI Associate question wrong?
Identify which AI Associate exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
3 more ways this is tested on AI Associate
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A financial services company uses Einstein AI to recommend credit limits. The model tends to assign lower limits to applicants from a certain region. Which action best aligns with ethical AI practices?
medium- A.Replace the AI model with a simpler rule-based system
- ✓ B.Investigate the data and model for bias, and adjust the model if necessary
- C.Manually increase credit limits for all applicants from that region
- D.Ignore the pattern since the model is statistically valid
Why B: Option B is correct because ethical AI requires investigating and mitigating bias in data and models. Option A (replacing with a rule-based system) does not address bias and may lose AI benefits. Option C (manually increasing limits for that region without analysis) could be arbitrary and not address systemic bias. Option D (ignoring the pattern) allows potential discrimination.
Variation 2. A company's Einstein Discovery model for customer lifetime value shows a significant correlation between predicted value and customer's postal code. The company is concerned about ethical implications. What is the most appropriate response?
hard- A.Remove the postal code field from the model immediately
- ✓ B.Investigate whether postal code is a proxy for protected attributes and, if so, consider retraining the model without it or with fairness constraints
- C.Add more demographic data to the model to improve its accuracy
- D.Ignore the correlation since the model is predicting business value, not demographic attributes
Why B: Option B (Investigate whether postal code is a proxy for protected attributes and, if so, consider retraining the model without it or with fairness constraints) is correct because postal code can be a proxy for race or income. Option A (removing postal code outright) may not be straightforward. Option C (ignoring correlation as coincidental) is unethical. Option D (adding more demographic data) could increase bias.
Variation 3. A company's Einstein Sentiment model is used to flag negative customer feedback. The model was trained on English reviews only. When deployed globally, it misclassifies positive reviews in Spanish as negative. What is the primary ethical concern?
medium- A.The model is not interpretable.
- B.The model has low accuracy for Spanish reviews.
- ✓ C.The model is unfair to Spanish-speaking customers.
- D.The model violates privacy regulations.
Why C: The primary ethical concern is fairness: the model was trained exclusively on English reviews, so it systematically misclassifies Spanish positive feedback as negative. This creates a disparate impact on Spanish-speaking customers, violating the principle of algorithmic fairness. The issue is not just low accuracy but an unjust bias that disadvantages a specific linguistic group.
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Last reviewed: Jun 23, 2026
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