AI Associate Ethical Considerations of AI Practice Test 2 — 15 Questions

Question 1

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.

Answer

Accept the bias as a reflection of historical data.

Answer

Remove the gender feature from the model.

Question 2

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.

Answer

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

Answer

Increase transparency by publishing the model's decision criteria.

Question 3

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.

Answer

Ensure the system achieves high accuracy and ignore other metrics.

Answer

Deploy immediately and monitor for issues.

Answer

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

Question 4

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.

Answer

Transparency

Answer

Accountability

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Privacy

Question 5

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.

Answer

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

Answer

Use a complex deep learning model for higher accuracy.

Answer

Remove feature importance to simplify the model.

Question 6

Which THREE factors should be considered when evaluating the fairness of an AI model?

Accepted Answer

Disparate impact ratio across groups.. Option A is correct because the disparate impact ratio measures whether an AI model's predictions disproportionately harm or benefit certain demographic groups, typically by comparing selection rates across groups. A ratio below 0.8 or above 1.25 is often considered evidence of adverse impact, making it a key quantitative fairness metric.

Answer

Overall accuracy on the test set.

Answer

Model training time.

Question 7

Which TWO practices help ensure accountability in AI systems?

Accepted Answer

Assign a human owner for each AI system.. Option B is correct because assigning a human owner for each AI system establishes clear accountability, ensuring that a specific individual is responsible for the system's behavior, decisions, and compliance with ethical guidelines. This practice aligns with the principle of human oversight, which is critical for maintaining trust and addressing failures in AI systems.

Answer

Blame the AI system for mistakes to protect employees.

Answer

Automate all decisions to eliminate human error.

Answer

Open-source the model code to share responsibility.

Question 8

Refer to the exhibit. The fairness evaluation shows a disparate impact of 0.85, equal opportunity difference of 0.12, and demographic parity difference of 0.18. Which fairness thresholds are violated?

Accepted Answer

Equal opportunity and demographic parity.. The correct answer is C because the fairness evaluation shows violations of both equal opportunity and demographic parity thresholds. The equal opportunity difference of 0.12 exceeds the commonly accepted threshold of 0.1, and the demographic parity difference of 0.18 exceeds the typical threshold of 0.1. Disparate impact of 0.85 is within the acceptable range (typically 0.8 to 1.25), so it is not violated.

Answer

Demographic parity only.

Answer

Equal opportunity only.

Answer

Disparate impact only.

Question 9

Refer to the exhibit. What is the most likely cause of the fairness issue?

Accepted Answer

The training data is imbalanced, causing the model to perform better on the majority group.. Option B is correct because imbalanced training data often leads to disparate performance. Option A is wrong because the model is not inherently biased. Option C is wrong because overall accuracy can be high despite bias. Option D is wrong because there is no indication of overfitting.

Answer

The model overfits to the male group.

Answer

The overall accuracy is too low.

Answer

The model is inherently biased against females.

Question 10

A company is deploying an AI-powered chatbot to handle customer service inquiries. The bot uses historical chat data for training. Which ethical consideration is MOST important to address before deployment?

Accepted Answer

Obtaining consent from customers whose data is used for training. The most critical ethical consideration is obtaining consent from customers whose historical chat data is used to train the chatbot. Under regulations like GDPR and CCPA, personal data (including chat transcripts) requires explicit consent for processing, especially when used to train an AI model. Deploying without consent violates data privacy laws and erodes user trust, regardless of the chatbot's technical performance.

Answer

Maximizing the chatbot's response accuracy

Answer

Ensuring the chatbot can handle high traffic volumes

Answer

Designing a human handoff protocol for complex issues

Question 11

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?

Accepted Answer

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.

Answer

The model may overfit to the majority population

Answer

The model cannot scale to real-time predictions

Answer

The model lacks explainability

Question 12

A company is designing an AI system to screen job applicants. To ensure fairness, which practice should be implemented?

Accepted Answer

Conduct regular fairness audits on model outcomes. Regular fairness audits are essential because they systematically evaluate model outcomes for bias across demographic groups, using metrics like disparate impact or equal opportunity difference. This practice aligns with responsible AI frameworks (e.g., NIST AI Risk Management Framework) and helps detect subtle biases that may emerge from proxy variables or data drift, ensuring the screening process remains equitable over time.

Answer

Use only one data source for consistency

Answer

Maximize the model's accuracy on historical hiring decisions

Answer

Remove all demographic data from the training set

Question 13

An AI system is used to approve loan applications. The model uses income, zip code, and credit score as features. What is a potential ethical concern?

Accepted Answer

Zip code may act as a proxy for race, leading to discrimination. Option A is correct because using zip code as a feature can introduce proxy discrimination. Zip codes are strongly correlated with race and socioeconomic status due to historical redlining and residential segregation. When the model learns patterns from zip code, it may inadvertently deny loans to applicants from certain racial or ethnic groups, violating fair lending laws and ethical AI principles.

Answer

The model should be a black box to avoid bias

Answer

Credit scores are rarely accurate

Answer

Income is not a reliable predictor of repayment

Question 14

A company deploys an AI recommender system that personalizes content. The system is trained on user click data. After deployment, the company notices that the system increasingly recommends sensationalist content, leading to user polarization. Which principle is being violated?

Accepted Answer

Beneficence. The recommender system's shift toward sensationalist content, which polarizes users, violates the principle of beneficence because it causes harm (user polarization) rather than promoting well-being. Beneficence requires AI systems to act in the best interests of users and society, not to optimize for engagement metrics at the expense of ethical outcomes.

Answer

Accuracy

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Privacy

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Transparency

Question 15

An AI model for predicting employee performance is found to have a higher false positive rate for women than for men. What is the best course of action?

Accepted Answer

Investigate the cause and retrain the model to reduce bias. Option A is correct because a higher false positive rate for women indicates the model has learned biased patterns from the training data, likely due to imbalanced or skewed historical data. Investigating the cause—such as examining feature correlations, data distribution, and model architecture—allows for targeted retraining (e.g., reweighting, adversarial debiasing, or fairness constraints) to reduce bias without sacrificing overall performance. This aligns with ethical AI principles and regulatory expectations, ensuring the model is fair across demographic groups.

Answer

Lower the decision threshold for women to equalize false positive rates

Answer

Proceed with deployment because the overall accuracy is acceptable

Answer

Use the model but require manual review for all female candidates