Pass AIGP Exam Guide

Risk probability/severity testing is not typically used to evaluate the performance of an AI system. While important for risk management, it does not directly assess an AI system's operational performance. Adversarial robustness, statistical sampling, and decision analysis are all methods that can help evaluate the performance of a responsible AI system by testing its resilience, accuracy, and decision-making processes under various conditions. Reference: AIGP Body of Knowledge on AI Performance Evaluation and Testing.

Testing data is a subset of data used to provide a robust evaluation of a final model. After training the model on training data, it is essential to test its performance on unseen data (testing data) to ensure it generalizes well to new, real-world scenarios. This step helps in assessing the model's accuracy, reliability, and ability to handle various data inputs. Reference: AIGP Body of Knowledge on Model Validation and Testing.

The White House Executive Order from November 2023 requires companies developing dual-use foundation models to report on their current training or development activities, the results of red-team testing, and the physical and cybersecurity protection measures. However, it does not mandate reports on environmental impact studies for each dual-use foundation model. While environmental considerations are important, they are not specified in this context as a reporting requirement under this Executive Order.

Human-centric AI focuses on improving the human experience by addressing individual needs and enhancing human capabilities. Option D exemplifies this by using virtual assistants to tailor educational content to each student's unique abilities and needs, thereby supporting personalized learning and improving educational outcomes. This use case directly benefits individuals by providing customized assistance and adapting to their learning pace and style, aligning with the principles of human-centric AI.