- A
Increase model regularization
Why wrong: Regularization prevents overfitting but does not directly mitigate bias.
- B
Remove the gender feature
Why wrong: Removing the feature is insufficient if other features are correlated with gender.
- C
Reduce model complexity
Why wrong: Reducing complexity may hurt accuracy without solving bias.
- D
Use adversarial debiasing technique
Adversarial debiasing explicitly reduces bias in learned representations.
Question 163 of 1,000
How to Use Adversarial Debiasing to Reduce Bias in AI Models
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?
Answer choices
Why each option matters
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 D is correct. Adversarial debiasing is a technique that explicitly reduces bias by training a model to prevent discrimination while maintaining accuracy. Option B (removing the gender feature) is not sufficient because other features may correlate with gender and perpetuate bias. Option A (increasing regularization) helps with overfitting, not bias. Option C (reducing complexity) may not address the root cause of bias.
Key principle: ACLs process entries top to bottom and stop at the first match. Entry order and interface direction matter as much as the permit or deny statement.
Answer analysis
Option-by-option breakdown
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.
Related concept
Standard ACLs match source addresses.
Common exam traps
Common exam trap: ACLs stop at the first match
ACLs are processed top to bottom. The first matching entry wins, and an implicit deny usually exists at the end.
Detailed technical explanation
How to think about this question
ACL questions test precision: source, destination, protocol, port and direction. A generally correct ACL can still fail if it is applied on the wrong interface or in the wrong direction.
KKey Concepts to Remember
- Standard ACLs match source addresses.
- Extended ACLs can match source, destination, protocol and ports.
- The first matching ACL entry is used.
- There is usually an implicit deny at the end.
TExam Day Tips
- Check inbound versus outbound direction.
- Read the ACL from top to bottom.
- Look for a broader permit or deny above the intended line.
Key takeaway
ACLs process entries top to bottom and stop at the first match. Entry order and interface direction matter as much as the permit or deny statement.
Real-world example
How this comes up in practice
A security administrator must allow nursing staff to reach a patient records server while blocking access from the guest Wi-Fi VLAN. After applying an extended ACL, traffic is still blocked from nursing workstations. The ACL was applied outbound instead of inbound on the wrong interface. Questions like this test ACL direction and placement rules.
What to study next
Got this wrong? Here's your next step.
Review ACL processing order, placement rules (standard near destination, extended near source), and inbound vs outbound direction. Study wildcard masks and implicit deny. Then practise related AI Associate ACL questions on filtering logic and placement.
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FAQ
Questions learners often ask
What does this AI Associate question test?
Ethical Considerations of AI — This question tests Ethical Considerations of AI — Standard ACLs match source addresses..
What is the correct answer to this question?
The correct answer is: Use adversarial debiasing technique — Option D is correct. Adversarial debiasing is a technique that explicitly reduces bias by training a model to prevent discrimination while maintaining accuracy. Option B (removing the gender feature) is not sufficient because other features may correlate with gender and perpetuate bias. Option A (increasing regularization) helps with overfitting, not bias. Option C (reducing complexity) may not address the root cause of bias.
What should I do if I get this AI Associate question wrong?
Review ACL processing order, placement rules (standard near destination, extended near source), and inbound vs outbound direction. Study wildcard masks and implicit deny. Then practise related AI Associate ACL questions on filtering logic and placement.
What is the key concept behind this question?
Standard ACLs match source addresses.
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Last reviewed: Jun 23, 2026
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