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While cost, speed, and infrastructure are practical considerations, the AAIA™ Study Guide stresses that ethical implications must be the top priority. Implementing AI without evaluating potential harm, bias, discrimination, or privacy concerns can result in legal, social, and reputational damage.

“Ethical implications should be central to AI implementation decisions. Responsible AI practices involve evaluating societal impact, transparency, accountability, and fairness before deployment.”

Therefore, D is the most essential consideration.

When organizations leverage off-the-shelf AI models, effective vendor management is critical to ensure operational reliability, compliance, and long-term support. The ISACA Advanced in AI Audit™ (AAIA™) Study Guide highlights that the "establishment of clear contractual terms regarding responsibilities for ongoing model updates, maintenance, support, and incident response is essential for managing third-party AI risks."

By clearly defining the roles and expectations for updates and support (option B), organizations reduce the risk of unaddressed vulnerabilities, outdated models, or unclear recourse in the event of an incident or system failure. This approach supports ongoing risk management and ensures that both parties understand their obligations throughout the model’s lifecycle.

While market research, vendor accreditation, and contract review by information security are important due diligence steps, they do not directly address the need for clarity in ongoing vendor responsibilities, which is critical for effective governance and sustained operation of AI solutions.

Human-centered design (HCD) focuses on integrating stakeholder needs, ethical implications, and social impact into AI system development. The AAIA™ Study Guide emphasizes the role of HCD in minimizing negative workforce impacts and ensuring inclusive design.

“Auditors should review whether the AI system design process included human-centered practices—particularly for applications affecting jobs or critical human roles. HCD ensures responsible adoption.”

While feedback collection (C) and impact assessments (D) provide useful context, A directly addresses ethical impact mitigation at the design level.

The AAIA™ Study Guide advises that if an AI model presents a risk that exceeds the organization's predefined risk tolerance—especially in cases of ethical harm or bias—deployment should be delayed until proper safeguards are in place. This approach prevents legal exposure and preserves stakeholder trust.

“When AI risks exceed acceptable thresholds, organizations must suspend implementation until corrective action reduces the risk to within tolerance levels. Proceeding without mitigation violates sound governance principles.”

While improving data (B) may help, it does not address the immediate governance concern. Risk tolerance (D) should not be adjusted to fit flawed systems. Thus, A is the correct course.