The answer is to retrain the model with a modified objective that penalizes age-based disparities. This approach directly addresses the ethical concern of age discrimination in AI triage systems by incorporating a fairness constraint into the model’s loss function, forcing it to balance predictive accuracy with equitable treatment across age groups. Simply removing the age feature would not fix the bias, as correlated variables like comorbidities or medication history can still proxy for age. On the Salesforce AI Associate exam, this scenario tests your understanding of algorithmic fairness and bias mitigation techniques, specifically how to adjust model objectives rather than relying on data masking or rebuilding from scratch. A common trap is assuming that removing a protected attribute eliminates bias, but the exam emphasizes that bias can persist through proxy features. Memory tip: think “penalize the pattern, not the person”—adjusting the objective directly targets the discriminatory pattern while preserving clinical effectiveness.
AI Associate Ethical Considerations of AI Practice Question
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 hospital uses an AI triage system to prioritize patients in the emergency department. The AI was trained on historical patient data and assigns priority scores based on vital signs and symptoms. Recently, a study finds that the system consistently assigns lower priority to elderly patients compared to younger patients with similar clinical presentations. The hospital's ethics committee is concerned about age discrimination. The current model achieves high accuracy in predicting outcomes, and doctors have come to rely on it for efficiency. What should the hospital do to address the ethical concern while maintaining clinical effectiveness?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Retrain the model with a modified objective that penalizes age-based disparities.
Option A is correct because adjusting the objective function to penalize age bias directly addresses the discrimination while keeping the model effective. Option B removes age, but bias may persist through correlated features. Option C requires building a new model from scratch, which is time-consuming and may not be necessary. Option D ignores the problem.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Replace the AI triage system with a completely new model built from scratch.
Why it's wrong here
This is costly, time-consuming, and may introduce other issues; modifying the existing model is more efficient.
✓
Retrain the model with a modified objective that penalizes age-based disparities.
Why this is correct
This approach minimizes bias while retaining the model's predictive power, aligning with fairness and accuracy.
Related concept
Static NAT maps one inside address to one outside address.
✗
Continue using the current model since it has high accuracy and efficiency.
Why it's wrong here
Ignoring age bias violates ethical guidelines and could lead to legal action.
✗
Remove age as an input feature from the model.
Why it's wrong here
Age removal may not eliminate bias if other features like prior conditions correlate with age.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
How this comes up in practice
A small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI Associate NAT questions on configuration and troubleshooting.
Ethical Considerations of AI — This question tests Ethical Considerations of AI — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Retrain the model with a modified objective that penalizes age-based disparities. — Option A is correct because adjusting the objective function to penalize age bias directly addresses the discrimination while keeping the model effective. Option B removes age, but bias may persist through correlated features. Option C requires building a new model from scratch, which is time-consuming and may not be necessary. Option D ignores the problem.
What should I do if I get this AI Associate question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI Associate NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Question Discussion
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