What is a challenge faced by internal auditors in AI auditing?

The absence of widely adopted audit precedents is indeed a significant challenge for internal auditors when it comes to AI auditing. This stems from the relatively nascent and rapidly evolving nature of AI technologies, which means that established best practices, standards, and methodologies for auditing these systems are still being developed. Unlike traditional auditing fields, where there are well-defined frameworks and historical benchmarks to draw from, auditors lack a robust foundation of precedents specific to AI. This makes it difficult for auditors to assess the effectiveness and compliance of AI systems, as they often have to create new strategies and techniques from scratch.

Furthermore, without widely adopted benchmarks, auditors may struggle with consistency in their evaluations and risk assessments, leading to potential disparities in audit quality and outcomes. This challenge emphasizes the need for the development of standardized approaches to AI auditing and highlights the importance of education and training in this emerging field.