AI0-001 AI Governance and Ethics Practice Question
A financial institution wants to use AI for loan approvals and must comply with fair lending laws. Which TWO practices should the institution adopt to mitigate bias and ensure compliance?
⚠ Common exam trap
AI0-001 often tests the misconception that removing sensitive features ('fairness through unawareness') eliminates bias, when proxy variables and historical bias in remaining features preserve discrimination.
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
✓
Apply fairness-aware machine learning techniques during model training
Option D is correct because fairness-aware machine learning techniques (e.g., pre-processing, in-processing, or post-processing bias mitigation methods such as reweighting, adversarial debiasing, or equalized odds) directly address bias during model training, which is essential for fair lending compliance. Option E is correct because disparate impact analysis quantifies whether model outcomes disproportionately disadvantage protected groups, a key requirement under fair lending laws like the Equal Credit Opportunity Act (ECOA) and Fair Housing Act. Option A is incorrect because removing all features except credit score does not eliminate bias—credit scores themselves can reflect historical discrimination—and it may violate fair lending rules by ignoring relevant factors. Option B is incorrect because black-box models without explainability hinder regulatory audits and adverse action explanations required by laws such as ECOA and the Fair Credit Reporting Act (FCRA). Option C is incorrect because using only demographic features would be both discriminatory and nonsensical for loan approval decisions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove all features except credit score to avoid bias
Why it's wrong here
Dropping every feature but credit score discards income, debt-to-income and employment data that fair lending assessment requires, and credit scoring alone can encode historical disparate impact. It is tempting because removing variables appears to eliminate the protected-attribute proxies that drive bias.
- ✗
Use a black-box model without explainability to protect intellectual property
Why it's wrong here
Opaque models cannot produce the adverse-action reasons and bias audits that fair lending regulators demand, so compliance cannot be demonstrated. It is tempting because black-box architectures protect proprietary scoring logic from disclosure to competitors and applicants.
- ✗
Use only demographic features to ensure equal treatment
Why it's wrong here
Training solely on demographic attributes makes decisions directly on protected characteristics, which fair lending law prohibits outright. It is tempting because equal demographic representation sounds like equal treatment, yet disparate-treatment rules forbid using those attributes as decision inputs.
- ✓
Apply fairness-aware machine learning techniques during model training
Why this is correct
Fairness-aware training constrains the model's objective function to reduce disparate impact across protected groups, directly addressing the fair lending requirement. By penalising biased outcomes during fitting rather than only auditing afterwards, it satisfies the stem's compliance constraint at the point where bias enters the model.
- ✓
Conduct disparate impact analysis on model outcomes
Why this is correct
Disparate impact analysis statistically compares approval rates across protected groups, exposing outcomes that breach fair lending law even when protected attributes were excluded. This satisfies the compliance requirement by detecting discriminatory effects in deployed model decisions.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 962 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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.