Fairness, Explainability, and Model Governance in ML Models

This chapter explores the concepts of fairness, explainability, and model governance in relation to ML models. It discusses the importance of ensuring equal performance for different groups and understanding feature importance in decision-making. The chapter also highlights the significance of model monitoring, governance, and the use of tools like Arise for monitoring biases in production.