Navigating the Challenges of AI Data Quality and Reliability

This chapter delves into the intricacies of deploying AI models, highlighting the necessity of high-quality data across diverse sectors like healthcare and fraud detection. It underscores the importance of data curation, the interplay between real and synthetic data, and the need for comprehensive testing and regulation to ensure reliable AI outcomes.