Auditing the Reliability of Generative AI Tools
Albert J. Marcella · 2025
This chapter explores the critical importance of auditing generative AI tools to ensure their reliability, accuracy, and compliance with ethical and regulatory standards. It highlights challenges such as managing biases, data quality, and performance stability in generative AI applications across sensitive industries like healthcare and law. Key audit focus areas include transparency, training data integrity, adaptability, and security, alongside questions to evaluate model robustness, error-handling mechanisms, and user feedback integration. These audits ensure that generative AI tools are dependable and foster accountability while mitigating risks related to their deployment.