Understanding and monitoring bias in machine learning models is crucial for ensuring fairness and compliance, especially as AI systems become more autonomous. The article discusses methods for identifying bias in both data and models, highlighting the importance of analyzing demographic information during training and deployment to avoid legal and ethical issues. It also introduces metrics and frameworks, such as those in AWS SageMaker, to facilitate this analysis and ensure equitable outcomes across different demographic groups.