Aligning impact assessment activities with regulatory and legal requirements ensures that high- risk third-party AI systems are evaluated rigorously and managed in compliance with relevant standards.

The correct answer is A because the first step in any AI project lifecycle is to clearly define the business objective and justify the use of AI. AI governance frameworks emphasize that planning begins with understanding the purpose, value, and necessity of the system before moving into design or risk assessment stages. Establishing a business case ensures alignment with organizational goals, identifies expected benefits, and evaluates whether AI is the appropriate solution. This step corresponds to the planning phase of the AI lifecycle, where objectives and intended outcomes are documented. In contrast, TEVV processes occur during development and testing, while impact assessments and redress considerations arise later as part of risk management and governance. Starting with a clear "why AI" foundation ensures responsible, efficient, and goal-oriented system development.

The Singapore Model AI Governance Framework recommends several measures to promote the responsible use of AI, such as determining the level of human involvement in decision-making, adapting governance structures, and establishing communications and collaboration among stakeholders. However, employing human-over-the-loop protocols is not specifically mentioned in this framework. The focus is more on integrating human oversight appropriately within the decision-making process rather than exclusively employing such protocols. Reference: AIGP Body of Knowledge, section on AI governance frameworks.

Importers of high-risk AI systems must verify that the system bears the CE marking and that the EU Declaration of Conformity has been issued by the provider. This is a direct obligation under the EU AI Act.

Under the EU AI Act, General-Purpose AI models with systemic risk are required to publish a detailed summary of the data used to train the model. This transparency measure ensures that users and regulatory bodies can assess the model's data sources and potential biases.