AI0-001 AI Security, Ethics and Governance Practice Question
A credit union uses an AI model to approve personal loans. The model was trained on historical data from the past five years. A recent internal review shows that the model approves loans predominantly for white applicants compared to other ethnicities, even when income and credit scores are similar. The credit union wants to comply with fair lending laws without significantly reducing overall approval rates. The data science team has access to the training data. What is the most appropriate remediation step?
⚠ Common exam trap
CompTIA often tests the misconception that adjusting the approval threshold (Option D) is a valid fairness intervention, but the trap here is that threshold adjustment only changes the cutoff for decisions without fixing the underlying biased feature representations, leading to inconsistent and potentially illegal outcomes under fair lending laws.
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
✓
Resample the training data to ensure balanced representation of ethnicities
Resampling the training data to ensure balanced representation of ethnicities directly addresses the root cause of the bias—skewed historical data—without altering the model's decision logic or approval thresholds. By rebalancing the dataset (e.g., oversampling underrepresented groups or undersampling the majority), the model learns from a more equitable distribution of features, reducing disparate impact while preserving overall approval rates. This approach aligns with fair lending laws by mitigating bias at the data level, which is the most fundamental and effective remediation step.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply a fairness constraint that penalizes the model for disparate impact
Why it's wrong here
Applying a fairness constraint that penalises the model for disparate impact would directly reduce approval rates for the majority group to achieve statistical parity, contradicting the requirement to avoid significantly lowering overall approval rates. This approach is tempting because it is a standard technique for enforcing demographic parity in classification models, and would be correct if the goal were to equalise approval proportions across ethnic groups regardless of overall rate impact.
- ✗
Discontinue the AI model and use manual approval for all loans
Why it's wrong here
Manual approval may be less efficient and still biased.
- ✓
Resample the training data to ensure balanced representation of ethnicities
Why this is correct
Resampling addresses the root cause by balancing training data.
- ✗
Adjust the approval threshold so that approval rates are equal across ethnic groups
Why it's wrong here
This could lead to unqualified approvals or rejections without fixing the model.
About these practice questions
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.