GDPR Right to Explanation for AI Recruitment Tools
An organization wants to ensure its AI systems comply with new regulations requiring explanations for automated decisions. Which governance practice is most directly relevant?
⚠ Common exam trap
The AI0-001 exam often tests the distinction between governance practices that are about oversight (ethics board) or data protection (differential privacy) versus those that directly implement a specific technical requirement (explainability), leading candidates to choose a broader or unrelated option.
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
✓
Deploying explainability tools
Deploying explainability tools (B) is the most directly relevant governance practice because the regulation specifically requires explanations for automated decisions. Explainability tools, such as LIME or SHAP, generate human-interpretable justifications for model outputs, enabling compliance with transparency mandates. This directly addresses the need to understand and communicate why a particular decision was made, unlike other practices that focus on privacy, fairness, or oversight.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Implementing differential privacy
Why it's wrong here
Differential privacy adds calibrated noise to outputs to protect individuals in training data; it produces no explanation of a decision. Explainability tooling such as SHAP or LIME addresses the regulatory requirement. Differential privacy would be right when the obligation is privacy protection.
- ✓
Deploying explainability tools
Why this is correct
Explainability tools generate the feature attributions and decision rationales that regulators require for automated decisions, directly satisfying the explanation mandate. Governance practice must therefore operationalise interpretability so each decision can be justified to auditors and affected individuals.
- ✗
Conducting bias audits
Why it's wrong here
Bias audits measure disparate impact across protected groups, not the reasoning behind an individual automated decision, so they cannot satisfy an explanation mandate. They are tempting because audits are a recognised AI governance control, and would be correct where the regulation targets discriminatory outcomes rather than explainability.
- ✗
Establishing an AI ethics board
Why it's wrong here
An ethics board sets principles and reviews dilemmas; it does not itself produce the per-decision explanation artefacts the regulation demands. It is tempting because boards do govern AI, and would be right when the requirement is broad policy oversight rather than documented reasoning for each automated outcome.
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 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.