Model governance is the framework of policies, processes, controls, and responsibilities used to manage AI and machine learning models throughout their lifecycle. It helps organizations ensure that models are developed, validated, deployed, monitored, and updated in a controlled and accountable way. Governance typically covers areas such as model approval, risk assessment, documentation, performance monitoring, data quality, security, compliance, and human oversight.
For example, a bank may use model governance to review and monitor a credit-scoring model before allowing it to influence lending decisions. This helps identify performance issues, bias, or regulatory concerns and ensures appropriate controls are in place. Model governance is useful for maintaining reliable AI systems, reducing operational and compliance risks, supporting responsible AI practices, and establishing clear accountability for model-related decisions.
