How can data science techniques be utilized to uncover biases in machine learning models?

As machine learning models are increasingly integrated into decision-making processes across industries, understanding and mitigating bias have become paramount. Bias can originate from various sources, including data collection and model training processes, and can lead to unfair or discriminatory outcomes. Data scientists are tasked with developing methodologies to detect and address these biases efficiently. This involves using a combination of exploratory data analysis, fairness metrics, and advanced techniques like adversarial testing and fairness-aware learning. How can these data science practices be systematically applied to uncover both overt and subtle biases within complex models? What role do tools and frameworks play in helping data scientists ensure that their models are not just accurate, but also equitable?