Modelreview

Modelreview is the systematic evaluation of a machine learning model to determine its readiness for deployment and to plan ongoing governance. It encompasses technical performance, safety, fairness, interpretability, and compliance with applicable policies and regulations.

A typical review defines objectives and success metrics, audits the training data, and tests the model under representative scenarios and potential distribution shifts. It assesses accuracy, calibration, and robustness, and examines fairness indicators such as bias across groups. Explainability and verifiability are evaluated through feature importance, explanations for decisions, and the availability of audit trails.

Governance artifacts such as model cards, datasets documentation, risk assessments, and a model registry are prepared, and a responsible-ai review board signs off before release. Post-deployment, monitoring, drift detection, and a retraining plan are part of ongoing modelreview to address changing data and user needs.

Challenges include data quality, representativeness, leakage, privacy concerns, and balancing transparency with confidentiality. Modelreview is a component of broader machine-learning governance and responsible-AI practices, designed to promote reliability, accountability, and ethical use of predictive systems.