IAPP Updated AIGP Exam Questions and Answers by ariya

The OECD ' s Ethical AI Governance Framework aims to ensure that AI development and deployment are carried out ethically while fostering innovation. The framework includes principles like transparency, accountability, and human rights protections to prevent societal harm. It does not focus solely on technical design or post-deployment monitoring (C), nor does it establish industry-specific requirements (B). While explainability is important, the primary goal is to balance innovation with ethical considerations (D).

Providing the loan applicants with information about the model capabilities and limitations would not directly support fairness testing by the compliance team. Fairness testing focuses on evaluating the model ' s decisions for biases and ensuring equitable treatment across different demographic groups, rather than informing applicants about the model.

External communication plans primarily establish how an organization communicates relevant AI information, risks, incidents, limitations, deployment changes, and governance matters to people outside the organization. A therefore provides the broadest and most accurate purpose. The current IAPP AIGP Study Guide expressly treats external communications as part of ongoing AI operations and associates them with transparency and trust-building. NIST AI RMF likewise requires communication plans for identified and measured AI risks and encourages communication with external stakeholders, affected communities, users, and other relevant AI actors. B concerns internal training rather than external communication. C belongs to third-party procurement and contract governance. Regulatory notification in D may form one component of an external communication plan when legally required, but it is considerably narrower than the overall governance objective of transparent stakeholder communication represented by A.

Differential privacy is a technique used to ensure that the inclusion or exclusion of a single data point does not significantly affect the outcome of any analysis, providing a way to mathematically prove that no specific piece of training data has more than a negligible effect on the model or its output. This is achieved by introducing randomness into the data or the algorithms processing the data. In the context of training large language models (LLMs), differential privacy helps in protecting individual data points while still enabling the model to learn effectively. By adding noise to the training process, differential privacy provides strong guarantees about the privacy of the training data.