CCNA Ethical Ai Questions

Option C is correct: Autonomy and privacy require obtaining explicit consent. Option A is wrong because transparency is about disclosure, not permission. Option B is wrong because accountability is about responsibility.

Option D is wrong because fairness is about impartiality.

Option A (Investigate the training data for biased labels or sampling bias) is correct because data is often the source. Option C (Engage local ethics representatives from affected regions) ensures diverse perspectives. Option E (Implement a fairness metric and set acceptable thresholds) provides quantitative governance.

Option B (Discontinue the model globally) may be too extreme. Option D (Use the model only in countries where it performs well) could still be unfair to others.

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.

Option B is correct because the demographic parity constraint has a threshold of 0.1, so a violation means the difference in selection rates across protected groups exceeded 0.1. Option A is wrong because accuracy is a separate metric. Option C is wrong retraining is not indicated.

Option D is wrong the exhibit does not mention removing attributes.

Option D is correct: Using an inherently interpretable model (e.g., logistic regression) can provide explanations. Option A is wrong because retraining the same model doesn't guarantee explainability. Option B is wrong because approximations may be inaccurate.

Option C is wrong because removing features doesn't address the need for explanations.

Option B is correct because the large accuracy gap (98% vs 80%) indicates the model performs poorly on negative text, which could lead to unfair treatment of users expressing negative sentiment. Option A is wrong because overfitting is not evident from this data. Option C is wrong even though bias score is below threshold, the significant accuracy disparity is a concern.

Option D is wrong while data imbalance is present, the disparate performance is the more direct ethical issue.

Option B is correct because lack of transparency (explainability: none) makes it impossible for users to understand or challenge decisions, which is a major ethical risk. Option A is wrong because accuracy is not indicated in the policy. Option C is wrong while data privacy is important, the policy does not mention privacy settings.

Option D is wrong performance is not addressed.

The correct answer is C because the model's underrepresentation leads to unfair outcomes for specific groups, violating the principle of fairness. Option A is wrong because transparency is about explainability, not outcome disparity. Option B is wrong because accountability refers to who is responsible, not the bias itself.

Option D is wrong because privacy is about data protection, not fairness.

The correct answer is A because investigating the data and model for bias is the appropriate ethical action. Option B is wrong because ignoring the bias violates fairness. Option C is wrong because immediately increasing rejections is unethical.

Option D is wrong because changing the screening criteria without analysis may not address the root cause.

Option B is correct because a simpler, interpretable model ensures transparency and reduces bias, aligning with ethical AI principles. Option A is wrong because post-hoc explanations may not be reliable or accepted by regulators. Option C is wrong because adjusting thresholds per group is discriminatory and illegal.

Option D is wrong because using the model on a subset does not resolve the underlying bias or compliance requirement.

Option D is correct because understanding the cause of disparity is essential before taking action. Option A is wrong because disabling the model may lose business value. Option B is wrong as simply retraining may not address root cause.

Option C is wrong because removing demographic features might not eliminate bias if correlated.

Option B is correct because the AI fails to provide an explanation for the decision, violating transparency. Option A is wrong accountability is about responsibility, but the issue is lack of explanation. Option C is wrong fairness is not directly addressed.

Option D is wrong safety is not relevant here.

Option A is correct because transparency is a key ethical principle; users should know they are interacting with AI. Option B is wrong because restricting to internal processes does not address ethical use. Option C is wrong because collecting excessive personal data violates privacy.

Option D is wrong because autonomous decisions may require human oversight.

Options B and D are correct because providing explanations and logging decisions are key to transparency. Option A is wrong because hiding the model reduces transparency. Option C is wrong because training on all data may embed biases, and does not directly relate to transparency.

Option E is wrong because consent is about privacy, not transparency.

Option B is correct because retraining with balanced data can help reduce disparate impact. Option A is wrong deploying with a ratio of 0.6 is likely illegal and unethical. Option C is wrong increasing accuracy threshold does not address fairness.

Option D is wrong removing the model may be too drastic without attempting mitigation.

Option D (Implement an AI ethics board with cross-functional stakeholders, conduct bias testing before deployment, and establish ongoing monitoring) is the most comprehensive. Option A (relying solely on Salesforce's built-in fairness tools) is insufficient without organizational governance. Option B (using anonymized data but not testing for proxy variables) might miss subtle biases.

Option C (only testing after complaints) is reactive, not proactive.

Option B is correct because doctors need explanations of predictions to make informed decisions and maintain accountability. Option A is wrong while precision is important, explanation is more critical for ethical use. Option C is wrong because low latency is a performance requirement, not ethical.

Option D is wrong because using external data may introduce privacy risks.

Option C is correct because transparency with customers about bot limitations is ethical and builds trust. Option A is wrong because humans cannot be replaced entirely in all cases. Option B is wrong as it hides the issue.

Option D is wrong because blame is not constructive.

Option A is correct: Data minimization and consent require that only necessary data is collected with permission. Option B is wrong because transparency involves disclosure, but the deeper issue is unauthorized collection. Option C is wrong because accountability is about responsibility.

Option D is wrong because fairness is about bias.

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.

Options B and D are correct. Testing the model on different demographic groups helps identify disparities, and involving diverse stakeholders brings multiple perspectives. Option A is wrong because removing all sensitive attributes may not eliminate bias due to proxy features.

Option C is wrong because a single metric cannot capture all fairness aspects. Option E is wrong because deploying quickly without testing can exacerbate unfairness.

Option D is correct: A diverse ethical review board and impact assessment can identify and mitigate unintended harm. Option A is wrong because removing AI entirely may be impractical. Option B is wrong because reducing engagement without analysis may be arbitrary.

Option C is wrong because more data doesn't guarantee less misinformation.

Option A is correct because re-weighting training samples can adjust for imbalanced representation. Option C is correct because adversarial debiasing reduces bias by learning unbiased representations. Option B is wrong regularization may not directly mitigate bias.

Option D is wrong increasing model depth can amplify bias. Option E is wrong removing all sensitive attributes may not eliminate bias due to proxy variables.

Options A and C are correct. Documenting model decisions provides an audit trail, and providing user-facing explanations helps users understand AI behavior. Option B is wrong because open-source algorithms do not guarantee transparency about specific model decisions.

Option D is wrong because collecting sensitive data may violate privacy principles. Option E is wrong because opt-out is about user control, not transparency.

Option A is correct because a code of ethics provides foundational principles. Option D is correct because regular audits ensure ongoing compliance. Option B is wrong while helpful, not always considered essential.

Option C is wrong using the latest algorithms is not an ethics component. Option E is wrong maximizing data collection contradicts ethical principles like privacy.

Option A is correct: Accountability means humans must oversee AI decisions and report issues. Option B is wrong because transparency is about disclosure. Option C is wrong because fairness is about bias.

Option D is wrong because privacy is about data protection.

Option B is correct because providing a detailed rationale supports explainability and transparency. Option A is wrong because black-box models lack interpretability. Option C is wrong because restricting use does not ensure explainability.

Option D is wrong because undocumented changes undermine accountability.

Option D is correct because flagging edge cases for human review efficiently focuses oversight on the most uncertain or risky decisions. Option A is wrong because post-hoc review for high-value loans may miss issues in other cases. Option B is wrong while thorough, it may be too slow for low-risk decisions.

Option C is wrong because full autonomy reduces human involvement.

Option B is correct because validating the relevance of social media data through rigorous analysis ensures that the factor is both fair and predictive. Option A abandons an innovative feature without evidence of harm. Option C may not be enough if the feature is irrelevant.

Option D is too narrow.

Option C is correct: Robustness and safety require that AI performs reliably under all foreseeable conditions. Option A is wrong because bias is about demographic fairness. Option B is wrong because transparency is about disclosure.

Option D is wrong because accountability is about oversight.

The correct answer is D because human oversight is critical for ethical AI, especially when decisions affect customers. Option A is wrong because training on all data may introduce bias. Option B is wrong because it is not always necessary to approve all refunds; the model should be accurate, not overly generous.

Option C is wrong because disabling logging reduces accountability.

Option B is correct because adding representative data from small businesses addresses the bias. Option A is wrong because ignoring small businesses is unethical. Option C is wrong because using a completely different model may create inconsistent experiences.

Option D is wrong because reducing complexity may not fix the data imbalance.

Option C is correct because fairness requires that AI systems do not discriminate against groups. Option A is wrong because transparency is about openness, not nondiscrimination. Option B is wrong because accountability is about responsibility.

Option D is wrong because privacy is about data protection.

The correct first step is to conduct a bias audit to identify sources of disparity. Option B is correct because ethical AI requires proactive bias detection. Option A (ignore) violates fairness.

Option C (retrain on more data of that group) might introduce bias. Option D (use a different model without audit) ignores the root cause.

Option B is correct because introducing a fairness check ensures that similar risk levels result in similar premiums across neighborhoods, addressing ethical concerns without discarding valid risk factors. Option A ignores the issue. Option C removes a potentially relevant factor, but may reduce accuracy.

Option D is a band-aid that doesn't fix underlying bias.

Options A, C, and E are correct. Monitoring for bias (A) ensures ongoing fairness, involving diverse stakeholders (C) reduces blind spots, and human oversight (E) ensures accountability. Option B is wrong because restricting data sources may limit representativeness.

Option D is wrong because accuracy should not override fairness.

The correct answer is B because the chatbot cannot explain its reasoning, violating transparency. Option A is wrong because privacy is not involved in explaining decisions. Option C is wrong because reliability is about accuracy, not explainability.

Option D is wrong because human oversight is about having humans in the loop, not about explaining decisions.

Option B is correct: Continuous monitoring and feedback loops can detect and correct drift or bias. Option A is wrong because removing the model does not solve underlying bias. Option C is wrong because past data already contains bias.

Option D is wrong because complete transparency does not automatically correct bias.

Option B is correct because conducting an AI ethics audit helps identify the root cause of bias and establish a baseline for mitigation. Option A is wrong because simply removing demographic data may not eliminate bias and could lose important context. Option C is wrong because increasing model complexity often exacerbates bias.

Option D is wrong because restricting to predefined responses limits the chatbot's utility and doesn't address underlying bias.

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.

Option B is correct because applying fairness constraints during training helps mitigate bias. Option A is wrong using biased data as is perpetuates bias. Option C is wrong discarding all biased variables may remove useful information and doesn't guarantee fairness.

Option D is wrong increasing model complexity can amplify bias.

Option B is correct because poor generalization across regions indicates the model is not generalizable. Option A is wrong overfitting would cause poor performance on new data within the same region. Option C is wrong privacy relates to data protection, not performance.

Option D is wrong accountability is about responsibility, not technical limitation.

Options A and C are correct: Obtaining explicit consent and enabling data deletion (right to erasure) are GDPR requirements. Option B (Storing data indefinitely) violates storage limitation. Option D (Selling data without permission) is illegal.

Option E (Processing data without consent) is not allowed.

Option B (Set up human review for recommendations that involve sensitive decisions) is correct for accountability. Option D (Regularly review recommendation logs for patterns of bias) is correct for monitoring. Option A (Allow the AI to act autonomously without human approval) reduces accountability.

Option C (Use the same recommendation for all customers) ignores personalization ethics. Option E (Hide the AI's reasoning from business users) violates transparency.

Option C is correct because transparency requires disclosing that the interaction is with an AI and allowing customers to opt for human assistance. Option A is wrong because it may not be feasible or ethical to force the chatbot to handle all queries. Option B is wrong because misrepresenting the AI as human is deceptive.

Option D is wrong because ignoring bias in training data is unethical.

Option B is correct because fairness requires that recommendations benefit the customer, not just the seller. Option A is wrong as transparency is about openness, not directly violated here. Option C is wrong because accountability involves responsibility, which may also be an issue but fairness is primary.

Option D is wrong because privacy is about data protection.

The correct answer is B because data privacy is critical; collecting only necessary data minimizes risk. Option A is wrong because historical data can contain bias. Option C is wrong because older data may be less relevant.

Option D is wrong because more features can increase privacy risks and complexity.

Option B is correct because continuous monitoring and adjustment is a key component of responsible AI. Option A is wrong because simply removing the geographic feature may not eliminate proxy variables. Option C is wrong because ignoring bias is unethical.

Option D is wrong because restricting usage may cause inequity.

Options A and D are correct. Option B is wrong because convenience sampling can introduce bias. Option C is wrong because ignoring small groups can perpetuate bias.

Option E is wrong because focusing only on high-accuracy groups may sacrifice fairness.

Transparency requires that the logic and outcomes of AI systems are understandable. Option A is correct because providing explanations for predictions allows users to understand and trust the AI. Option B (keeping proprietary algorithms secret) hinders transparency.

Option C (using complex models without explanation) obscures decision-making. Option D (only reporting accuracy metrics) does not explain specific decisions.

Option A (Investigate the data and model for bias, and adjust the model if necessary) is correct because ethical AI requires proactive identification and mitigation of bias. Option B (ignoring the pattern) could allow discrimination. Option C (increasing limits for that region without analysis) may not be justified.

Option D (using a different AI model without investigation) does not address the root cause.

Option B is correct because implementing a fairness metric like equal opportunity ensures the model does not disadvantage a protected group. Option A would perpetuate inequity. Option C removes a feature that may still allow bias through correlated variables.

Option D abandons a potentially useful tool without addressing bias.

The configuration lacks human override and does not require explanations, which undermines accountability. Option C (Accountability) is correct. Option A (Fairness) is not directly addressed but audit trail may help.

Option B (Privacy) is not mentioned. Option D (Transparency) is partially addressed by audit trail but not explanation. The absence of human override is a key accountability gap.

Option D is correct because price discrimination based on customer data is unfair and often unethical. Option A is wrong because personalization is about tailoring experience, not price gouging. Option B is wrong as dynamic pricing is legal but can be unethical.

Option C is wrong because transparency issue is secondary.

Option B is correct because implementing fairness constraints ensures recommendations are not systematically skewed, while still allowing personalization. Option A prioritizes revenue over ethics. Option C is too drastic and would lose benefits.

Option D reduces personalization and may not be effective.

Option B is correct because retraining with balanced data mitigates the representation bias, addressing the root cause. Option A ignores the fairness issue. Option C removes a feature that may be a proxy for other factors, but it may not eliminate bias if other correlated features remain.

Option D adjusts thresholds only for some groups, which could be considered unfair and may not be accepted by regulators.

Option B is correct because PII leakage violates privacy. Option A is wrong fairness is about bias. Option C is wrong transparency is about openness.

Option D is wrong accountability is about responsibility, but the immediate violation is privacy.

Option B is correct because a comprehensive audit and retraining with diverse data addresses the bias at the root. Option A gives special treatment that could be seen as unfair. Option C removes moderation, risking harmful content.

Option D does not solve the underlying bias.