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.
A financial institution uses AI for loan approvals. They notice the model is denying loans to women more often. After retraining with balanced data, the disparity persists. What is the next best step?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Increase model regularization
Why wrong: Regularization prevents overfitting but does not directly mitigate bias.
Remove the gender feature
Why wrong: Removing the feature is insufficient if other features are correlated with gender.
Reduce model complexity
Why wrong: Reducing complexity may hurt accuracy without solving bias.
Use adversarial debiasing technique
Adversarial debiasing explicitly reduces bias in learned representations.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use adversarial debiasing technique
Option B is correct because adversarial debiasing directly reduces bias in model representations. Option A is wrong because removing the gender feature may not eliminate bias if other features correlate. Option C is wrong because regularization helps control overfitting, not bias. Option D is wrong because reducing complexity may not address bias.
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
For each option: why learners choose it and why it is or isn't the right answer here.
Increase model regularization
Why it's wrong here
Regularization prevents overfitting but does not directly mitigate bias.
Remove the gender feature
Why it's wrong here
Removing the feature is insufficient if other features are correlated with gender.
Reduce model complexity
Why it's wrong here
Reducing complexity may hurt accuracy without solving bias.
Use adversarial debiasing technique
Why this is correct
Adversarial debiasing explicitly reduces bias in learned representations.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
Common exam traps
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
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.
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
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
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.
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FAQ
Ethical Considerations of AI — This question tests Ethical Considerations of AI — Static NAT maps one inside address to one outside address..
The correct answer is: Use adversarial debiasing technique — Option B is correct because adversarial debiasing directly reduces bias in model representations. Option A is wrong because removing the gender feature may not eliminate bias if other features correlate. Option C is wrong because regularization helps control overfitting, not bias. Option D is wrong because reducing complexity may not address bias.
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.
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Static NAT maps one inside address to one outside address.
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Last reviewed: Jun 23, 2026
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