AI auditing can cover various aspects of your AI systems, depending on your goals, context, and requirements. For example, it's important to audit the data you use to train and deploy your AI models for quality, relevance, diversity, representativeness, security, and privacy. Additionally, you should evaluate the algorithms, frameworks, and parameters used to build and optimize your AI models for accuracy, robustness, scalability, interpretability, and explainability. Furthermore, you should audit the results or actions produced by your AI models for validity, reliability, consistency, fairness, and impact. Finally, you should audit the workflows and tools used to design and monitor your AI systems for efficiency, effectiveness, alignment, and governance.