Precisionmeasures the proportion of positive predictions that were actually correct. When avoidingfalse positivesis the priority (e.g., fraud accusations, security alerts, loan denials), precision is the critical metric because:
* High precision = very few false positives
* Low precision = many false positives
AAIA stresses using appropriate performance metrics depending on risk context.
Accuracy (B) can mask imbalances.
Recall (D) relates to false negatives.
F1 (C) balances precision and recall but does not emphasize false positives specifically.
References:
AAIA Domain 2: Model Performance Metrics and Risk-Based Evaluation

When training data validation is inconsistent, the most severe risk is that the AI model may learn from incorrect, incomplete, biased, or corrupted data. This directly leads to a degradation of system reliability (option C), which manifests as inaccurate predictions, higher error rates, bias, or unstable behavior.
AAIA emphasizes that data validation prior to retraining is one of the most important controls because model behavior is fully dependent on training data integrity. If the quality and correctness of the data cannot be guaranteed, the resulting model outputs become unreliable, which can undermine compliance, operational decisions, and user trust.
Option A is less critical because increased complexity is not the core risk. Option B is important but secondary; documentation issues do not inherently degrade model reliability. Option D is an efficiency issue, not a risk to output integrity.
Therefore, compromised reliability due to poor-quality training data is the most significant risk.
References:
AAIA Domain 2: Data Management Specific to AI (data validation, verification, data quality).
AAIA Domain 1: Governance and Risk Controls for AI.

Excessive agencyoccurs when AI systems act too autonomously, making decisions without appropriate human oversight. To mitigate this in a browser extension, the BEST approach is tominimize functionality (A), ensuring the extension performs only narrowly defined, controlled actions. This aligns with AAIA's guidance onlimiting AI autonomy, applying least-privilege principles, and restricting unnecessary actions.
Removing user access entirely (B) is overly restrictive. Maximizing functionality (C) increases risk. Open- source extensions (D) improve transparency but do not inherently control excessive agency.Scope limitation is the most targeted and effective mitigation.
References:
ISACA,AAIA Exam Content Outline- Domain 1: Governance, AI Autonomy, and Risk Controls.

A steering committee is responsible for enterprise-wide strategic decisions, technology alignment, investment prioritization, and governance oversight.
AAIA states that AI implementation roadmaps must be driven by a group that can evaluate:
* Organizational strategy
* Technology readiness
* Budget allocations
* Risk appetite
* Regulatory obligations
* Cross-functional impacts
The risk committee (A) focuses only on risk, not implementation. Product management (C) has a narrow scope. Internal audit (D) evaluates controls, not roadmaps.
Thus, the steering committee is the appropriate authority.
References:
AAIA Domain 1: AI Governance Structures
AAIA Domain 4: Strategic Alignment and Oversight

Bias in AI decision-making is one of the most critical risks, particularly when AI influences areas like hiring, lending, or healthcare. The AAIA™ Study Guide highlights the ethical and operational consequences of biased models, which may lead to discrimination, legal liability, and reputational damage.
"Bias in AI outputs can originate from skewed training data or flawed algorithms. Auditors must evaluate whether mitigation techniques, such as bias detection and fairness testing, are in place." While costs (A) and industry maturity (B) are considerations, they do not pose the same systemic ethical risk.
Resistance (D) is a change management issue. C represents the most impactful and widespread risk.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Bias and Fairness in AI"