AI Associate Ethical Considerations of AI — 100 Questions

Question 1

A company uses Einstein Prediction Builder to recommend products. They notice the model often recommends high-priced items to users in affluent areas, potentially excluding others. What should the AI Associate do first?

Accepted Answer

Check the training data for representation and bias.. The correct first step is to check the training data for representation and bias because the model's tendency to recommend high-priced items to affluent areas suggests the training data may be skewed or contain historical biases. Einstein Prediction Builder relies on historical data to learn patterns, and if the data over-represents affluent users or under-represents others, the model will perpetuate those biases. Auditing the data for fairness and representation is the foundational step before any remediation, as per responsible AI practices.

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Remove the model from production immediately.

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Ignore the issue because the model predictions are accurate overall.

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Add more features about customer income.

Question 2

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?

Accepted Answer

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.

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The bot is routing to the wrong department.

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The escalation threshold is set too low.

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The bot is not properly connected to the escalation queue.

Question 3

A healthcare organization uses Einstein Discovery to predict patient readmission risk. The model uses protected attributes like race and age as features. Which action best aligns with Salesforce's ethical AI principles?

Accepted Answer

Retain the features but monitor for disparate impact and ensure compliance with regulations.. Removing protected attributes is a common step, but if they are proxies for legitimate medical factors, they may be retained with monitoring. Option A is too aggressive. Option C ignores that age can be medically relevant. Option D violates transparency and accountability.

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Remove race and age features entirely to ensure fairness.

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Replace age with an age group bucket to reduce granularity.

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Use the model as is because predictions are accurate.

Question 4

A sales team uses Einstein Lead Scoring. They notice the model gives low scores to leads from certain industries. The AI Associate suspects bias. What should they do to validate?

Accepted Answer

Analyze the distribution of scores across industry segments.. Option D is correct because analyzing the distribution of scores across industry segments directly validates whether the model exhibits systematic bias. By comparing score distributions, the associate can identify if certain industries are consistently under-scored, which would indicate a biased pattern rather than random variation. This approach aligns with ethical AI practices that require transparency and fairness assessment before any model adjustments.

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Run a holdout test to check prediction accuracy.

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Retrain the model with balanced data.

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Review the model's confidence intervals.

Question 5

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?

Accepted Answer

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.

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Evaluating model performance across different groups.

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Excluding gender from the model features.

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Documenting model limitations and assumptions.

Question 6

A financial services firm uses Einstein Next Best Action to offer credit products. The model recommends high-interest loans more often to minority groups. The AI Associate must mitigate this. What is the most effective approach?

Accepted Answer

Apply post-processing fairness adjustments to the recommendations.. Option C is correct because post-processing fairness adjustments directly modify the model's output to enforce demographic parity or equal opportunity, reducing biased recommendations without retraining the model. This approach is practical when the firm cannot easily change the underlying training data or model architecture, and it allows the AI Associate to intervene at the decision point to ensure fair lending practices.

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Remove the model and use a rule-based system.

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Use SHAP values to explain predictions.

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Add a disclaimer that recommendations may be biased.

Question 7

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?

Accepted Answer

The model is unfair to Spanish-speaking customers.. 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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The model is not interpretable.

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The model has low accuracy for Spanish reviews.

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The model violates privacy regulations.

Question 8

Which TWO actions help ensure transparency in AI systems according to Salesforce's ethical AI guidelines?

Accepted Answer

Documenting model assumptions and limitations.. Option D is correct because documenting model assumptions and limitations is a core transparency practice under Salesforce's ethical AI guidelines. It ensures stakeholders understand the boundaries and potential biases of the AI system, enabling informed trust and accountability.

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Limiting access to model outputs to only a few people.

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Using complex deep learning models without explanation.

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Automatically retraining models weekly.

Question 9

Which THREE factors should an AI Associate consider when evaluating a model for potential bias?

Accepted Answer

Whether features are correlated with protected attributes.. Option B is correct because if a feature is correlated with a protected attribute (e.g., race, gender, age), the model may inadvertently learn and perpetuate discriminatory patterns, even if the protected attribute itself is not used as an input. This is a key source of indirect or proxy bias in machine learning systems.

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The complexity of the model architecture.

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The date the model was last deployed.

Question 10

Which TWO practices are recommended when using AI for automated decision-making in hiring?

Accepted Answer

Regularly audit the model for adverse impact.. Option B is correct because regular auditing for adverse impact is a core ethical practice to detect and mitigate bias in AI-driven hiring systems. Audits involve statistical analysis (e.g., the four-fifths rule from the Uniform Guidelines on Employee Selection Procedures) to compare selection rates across protected groups, ensuring the model does not disproportionately disadvantage certain demographics.

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Use the AI model as the sole decision-maker.

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Use all available data including protected attributes.

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Ignore adverse impact if the model is accurate.

Question 11

An AI Associate reviews the Lead Scoring model exhibit. What is the primary ethical concern with this model?

Accepted Answer

The model has low recall, potentially missing minority class leads.. The primary ethical concern is that the model has low recall, meaning it fails to identify a significant portion of actual positive leads (the minority class). In a lead scoring context, this can result in missed business opportunities and potential bias against certain customer segments, as the model systematically overlooks valuable leads that do not fit the majority pattern.

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The model uses too many features.

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The model is not explainable.

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The training data is imbalanced.

Question 12

An AI Associate reviews the bot configuration and test results. Which action best addresses the ethical issue?

Accepted Answer

Add Spanish language support with separate sentiment model and intents.. The bot underperforms for Spanish speakers, so adding a Spanish sentiment model and intents would improve fairness. Option A does not fix the disparity. Option C may not solve the root cause. Option D is about privacy, not the language issue.

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Increase the fallback threshold for all languages.

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Collect more data from Spanish-speaking customers and retrain the English model.

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Disable sentiment analysis for non-English conversations.

Question 13

A healthcare organization is deploying an AI model to predict patient readmission risk. The model was trained on historical data that underrepresented minority populations. During testing, the model shows lower accuracy for those groups. What should the data scientist do first?

Accepted Answer

Re-evaluate the training data to ensure balanced representation and consider re-sampling techniques.. Option D is correct because the first step in addressing model bias is to audit the training data for representational imbalance. Re-evaluating the data and applying re-sampling techniques (e.g., oversampling minority groups or undersampling the majority) directly targets the root cause of the disparity—skewed class distributions—before modifying the model or its features.

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Remove sensitive attributes like race and gender from the training data.

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Ignore the disparity because the model's overall accuracy is acceptable.

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Retrain the model with more complex algorithms to improve accuracy.

Question 14

A financial institution uses an AI system to approve loan applications. The system denies loans to applicants from certain postal codes at a higher rate. The model includes 'postal code' as a feature. Which ethical consideration is most directly violated?

Accepted Answer

Fairness. The AI system's use of 'postal code' as a feature leads to disparate impact on applicants from certain areas, directly violating the ethical principle of fairness. Fairness requires that AI models do not discriminate against protected groups or perpetuate systemic biases, even if the feature itself is not a protected attribute. By denying loans at a higher rate based on postal code, the system is likely engaging in proxy discrimination, which is a core fairness violation.

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Privacy

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Accountability

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Transparency

Question 15

A company is developing a chatbot for customer service. They want to ensure the bot does not generate offensive responses. Which practice should they implement?

Accepted Answer

Deploy a content filter and human-in-the-loop moderation for sensitive interactions.. Option D is correct because deploying a content filter combined with human-in-the-loop moderation provides both automated detection of offensive language and human oversight for ambiguous or sensitive interactions. This layered approach ensures that the chatbot can block clearly toxic outputs in real time while allowing human reviewers to handle edge cases, reducing the risk of generating offensive responses.

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Use unsupervised learning to allow the bot to learn naturally from conversations.

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Remove all toxicity detection to avoid false positives.

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Train the model only on customer service transcripts without review.

Question 16

An AI system recommends job candidates to recruiters. The system was trained on resumes of past successful hires, most of whom were male. As a result, it consistently ranks female candidates lower. What is the most appropriate mitigation?

Accepted Answer

Re-sample the training data to include more female candidates and use fairness-aware algorithms.. Option C is correct because ensuring gender balance in training data addresses the root cause. Option A is wrong because removing gender may not eliminate proxy variables like 'years of experience gaps.' Option B is wrong because ignoring the issue perpetuates bias. Option D is wrong because post-processing adjustments may not be sufficient without data changes.

Answer

Add a post-processing adjustment to increase female candidates' scores.

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Accept the bias as a reflection of historical data.

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Remove the gender feature from the model.

Question 17

A credit scoring AI uses 50 features including zip code, age, and income. The model has high accuracy but denies credit disproportionately to a protected group. An audit reveals that zip code is a proxy for race. What is the best course of action?

Accepted Answer

Replace zip code with more relevant non-discriminatory features and retrain with fairness constraints.. Option B is correct because replacing biased proxy with more relevant features can maintain accuracy while reducing discrimination. Option A is wrong because simply removing zip code may not eliminate all proxies. Option C is wrong because retraining with same data yields same bias. Option D is wrong because transparency alone doesn't fix bias.

Answer

Remove zip code from the feature set and retrain.

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Keep zip code but add a fairness penalty to the loss function.

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Increase transparency by publishing the model's decision criteria.

Question 18

A company wants to deploy an AI system that makes hiring decisions. To comply with ethical guidelines, what should they do before deployment?

Accepted Answer

Conduct an ethics review and perform bias testing on diverse datasets.. Option A is correct because conducting an ethics review and performing bias testing on diverse datasets are essential steps to identify and mitigate potential discriminatory outcomes in AI-driven hiring systems. This aligns with ethical AI frameworks that require fairness, accountability, and transparency before deployment, ensuring the model does not perpetuate historical biases or violate anti-discrimination laws.

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Ensure the system achieves high accuracy and ignore other metrics.

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Deploy immediately and monitor for issues.

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Test the system only on a small dataset to expedite launch.

Question 19

An AI system used for medical diagnosis has been shown to have lower accuracy for certain ethnic groups. The development team is considering releasing it anyway because most patients are from the majority group. Which ethical principle is most compromised?

Accepted Answer

Fairness. The scenario describes an AI system that performs worse for certain ethnic groups, yet the team plans to release it anyway because the majority group is unaffected. This directly violates the principle of fairness, which requires that AI systems do not discriminate or perpetuate bias against any group. Releasing a model with known accuracy disparities without mitigation prioritizes overall performance over equitable treatment, compromising fairness.

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Transparency

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Accountability

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Privacy

Question 20

Which TWO actions best promote transparency in an AI system?

Accepted Answer

Publish an audit trail of model inputs and decisions.. Option B is correct because publishing an audit trail of model inputs and decisions enables external verification of the AI system's behavior, which is a core requirement for transparency. This allows stakeholders to trace how specific inputs led to particular outputs, ensuring accountability and facilitating debugging or compliance audits.

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Limit access to the model's logic to protect intellectual property.

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Use a complex deep learning model for higher accuracy.

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Remove feature importance to simplify the model.