AI0-001 AI Security, Ethics and Governance Practice Question
A retail company wants to ensure its AI-driven pricing algorithm does not discriminate against customers in protected groups. Which governance practice should be implemented first?
⚠ Common exam trap
The trap here is equating transparency or larger datasets with fairness, when the first required step is a structured bias assessment.
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
✓
Conduct a bias impact assessment before deployment.
A bias impact assessment is the proactive governance practice that systematically evaluates whether the pricing algorithm disadvantages protected groups. It informs mitigation before deployment, whereas code publication, more data, or reactive monitoring do not directly prevent discriminatory outcomes.
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 the model's training data volume.
Why it's wrong here
More data can improve accuracy but does not guarantee fairness; biased data at scale can amplify discrimination. Without assessing representation and outcomes, additional data may worsen disparities. Volume alone is not a governance control and should not precede a bias impact assessment.
- ✗
Deploy the model and monitor complaints.
Why it's wrong here
Reactive monitoring after deployment allows harm to occur before detection. Governance best practice requires proactive assessment to prevent discrimination. Complaint monitoring is useful as a secondary control but cannot replace pre-deployment bias evaluation, especially for pricing where harm can be immediate and widespread.
- ✓
Conduct a bias impact assessment before deployment.
Why this is correct
A bias impact assessment is a proactive governance step that identifies and mitigates discriminatory effects before the model affects customers. It examines training data, features, and outcomes for disparate impact, aligning with ethical AI principles. Performing it first prevents harm and provides documentation for regulators, making it the foundational practice for fair pricing.
- ✗
Publish the algorithm's source code publicly.
Why it's wrong here
Publishing source code may improve transparency but does not by itself detect or correct bias. It can also expose proprietary logic and attack vectors. Without a structured assessment, public code does not ensure fair outcomes and may not be meaningful to non-technical stakeholders. It is not the first governance step for preventing discrimination.
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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.