In AI governance discussions, what is often a necessity for model performance assessment?

The necessity for model performance assessment within AI governance discussions often leads to emphasizing the importance of publicly available audit reports. These reports provide transparency and accountability regarding how AI models operate, their efficacy, and the ethical considerations surrounding their use. By making audit reports accessible, stakeholders, including regulatory bodies, users, and the public, can better understand the performance and impact of AI systems. This openness fosters trust and allows for independent verification of a model's claims and behaviors, which is essential for responsible AI deployment.

Publicly available audit reports enable third-party assessments, facilitate regulatory compliance, and help identify potential biases or shortcomings in the model's design and implementation. Thus, they play a crucial role in ensuring that AI technologies are used ethically and effectively, enhancing the overall governance framework within which AI operates.

In contrast, the other options focus on aspects that do not directly contribute to performance assessment. Confidentiality of model architecture does not support transparency; complete automation may overlook the necessity for human oversight and evaluation; and trade secrets protection could hinder openness, which is vital for assessing performance reliably.