- A
The sentiment model was trained on a non-representative dataset.
Training data lacking linguistic diversity causes biased sentiment detection.
- B
The bot is routing to the wrong department.
Why wrong: Routing is not the cause of disproportionate escalation.
- C
The escalation threshold is set too low.
Why wrong: Threshold affects volume, not demographic disparity.
- D
The bot is not properly connected to the escalation queue.
Why wrong: The bot escalates, so connection works.
Question 489 of 1,000
Bias in Sentiment Analysis by Language or Accent
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 deploys an Einstein Bot that uses sentiment analysis to escalate frustrated customers. After launch, the bot escalates disproportionately for non-native English speakers. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
✓
The sentiment model was trained on a non-representative dataset.
Option A is correct because the sentiment analysis model likely exhibits bias due to training data that does not adequately represent the linguistic patterns, idioms, or expressions of non-native English speakers. This causes the model to misinterpret neutral or positive statements from these users as negative or frustrated, leading to disproportionate escalations. A non-representative dataset is a common source of algorithmic bias in AI systems.
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 sentiment model was trained on a non-representative dataset.
Why this is correct
Training data lacking linguistic diversity causes biased sentiment detection.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The bot is routing to the wrong department.
Why it's wrong here
Routing is not the cause of disproportionate escalation.
- ✗
The escalation threshold is set too low.
Why it's wrong here
Threshold affects volume, not demographic disparity.
- ✗
The bot is not properly connected to the escalation queue.
Why it's wrong here
The bot escalates, so connection works.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the concept that bias in AI systems typically originates from the training data or model design, not from operational configuration issues like thresholds or routing, which are common distractors.
Detailed technical explanation
How to think about this question
Sentiment analysis models often rely on word embeddings and n-gram features that are sensitive to syntactic variations common in non-native speech, such as omitted articles or unconventional word order. If the training dataset is predominantly composed of native English speakers, the model's decision boundary will be calibrated to their patterns, causing false positives for non-native inputs. This is a form of representation bias that can be mitigated by stratified sampling or domain adaptation techniques during training.
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 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.
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.
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Ethical Considerations of AI — study guide chapter
Learn the concepts, then practise the questions
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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: The sentiment model was trained on a non-representative dataset. — Option A is correct because the sentiment analysis model likely exhibits bias due to training data that does not adequately represent the linguistic patterns, idioms, or expressions of non-native English speakers. This causes the model to misinterpret neutral or positive statements from these users as negative or frustrated, leading to disproportionate escalations. A non-representative dataset is a common source of algorithmic bias in AI systems.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
2 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 Salesforce customer uses Einstein Sentiment Analysis to analyze customer feedback. They find the model is less accurate for non-English languages. What ethical concern does this raise?
easy- ✓ A.Bias
- B.Accountability
- C.Privacy
- D.Security
Why A: The varying accuracy across languages indicates bias in the model, which is a fairness concern.
Variation 2. A company uses Einstein Sentiment to analyze customer feedback. The tool incorrectly flags negative sentiment for customers with heavy accents. Which ethical issue is present?
easy- A.Privacy violation
- ✓ B.Bias and discrimination
- C.Accountability gap
- D.Lack of transparency
Why B: Option B is correct because the tool showing bias against accents constitutes bias and discrimination. Option A is incorrect as privacy is about data protection. Option C is incorrect as accountability is about responsibility. Option D is incorrect as transparency involves explaining decisions.
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Last reviewed: Jun 30, 2026
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