Navigating AI Model Development: Key Risks and Audit Considerations for Assurance Professionals

This article delves into the critical risks inherent in the AI model development stage, moving beyond data-centric issues to focus on the model's construction and governance. For audit and assurance professionals, understanding these risks—such as overfitting, lack of explainability, brittleness, and poor version control—is crucial for evaluating the reliability, compliance, and ethical implications of AI systems before they reach deployment. The piece emphasizes that seemingly successful development can mask significant vulnerabilities, underscoring the need for robust audit scrutiny.