This talk delves into the urgent need for robust regulatory frameworks to ensure the ethical use of machine learning (ML) systems across critical sectors like retail, healthcare, finance, and criminal justice. As these models influence major decision-making processes, it becomes vital to assess and mitigate the risks associated with their deployment, including issues of discrimination, opacity, and unintended harm. Real-world data plays a crucial role in evaluating the representativeness and validity of ML models, underlining the need for clear guidelines for application in a regulatory framework. This talk focuses on integrating demographic data and statistical techniques into a regulatory framework to address these challenges, emphasizing the importance of equity, accountability, and transparency in ML practices. By incorporating demographic techniques, the talk advocates for more equitable and reliable ML models that are less prone to bias, ensuring they align with societal norms and legal standards.