What types of data are primarily collected for a Generative AI audit?

The correct answer includes Audit Data and Feedback Data, as these types of data are critical for conducting a comprehensive audit of Generative AI systems. Audit Data captures the transactional records and operations that have occurred within the AI system. This data helps assess compliance, identify anomalies, and understand the processes the AI has executed.

Feedback Data is essential for evaluating the performance of the Generative AI system from the user perspective. This data provides insights into how well the AI is functioning in real-world scenarios and whether it meets the expectations of users and stakeholders. Collecting both types of data allows for a thorough assessment of the AI's effectiveness, reliability, and adherence to ethical standards.

In contrast to this, the other options represent different categories of data that may not encompass the full scope of requirements for a Generative AI audit. For example, while Performance Data and Usage Data provide insights into the system's functioning and user engagement respectively, they do not specifically address compliance or user satisfaction directly. Similarly, Raw Data and Processed Data focus more on the data lifecycle rather than the auditing aspect, and Input Data and Output Data deal with the flow of information through the system rather than the necessary evaluation criteria for an audit. Collectively, Audit and Feedback Data are more aligned with the