Model governance defines processes, roles and rules for the safe, transparent and accountable use of models, particularly machine learning models. It aims at compliance, reproducibility and continuous monitoring across the model lifecycle. Implementation requires clear policies, assigned responsibilities and technical tool support.
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Model governance is the set of rules and responsibilities an organization uses to control model development, approval, use, and oversight. It connects policies, roles, evidence, and escalation paths.
It grew from governance and risk-control practices in regulated organizations and expanded with data-driven and AI-based decisions. Principles such as the OECD AI Principles steer this evolution toward transparency, robustness, accountability, and human-centered use.
Governance is a guardrail around the model lifecycle: before use, purpose, data, and risks are checked; during operation, decisions and deviations remain traceable; when issues arise, defined owners and correction paths take over.
Roles identify who owns model purpose, quality, approval, and consequences.
Control points cover development, validation, use, change, and retirement.
Documentation and logs make data, decisions, and model changes auditable.
Model governance enables dependable decisions about which models may be used, under what conditions, and when they must be monitored or withdrawn.
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