Fairness in AI should be tested and monitored regularly throughout the entire lifecycle of the model. During development, fairness should be checked at multiple stages, including data collection, model training, and evaluation, to identify and address any biases. Before deployment, testing is crucial to ensure that the model’s decisions are equitable across different groups, preventing discrimination in real-world applications. Once deployed, continuous monitoring is essential to track how the model's predictions or decisions evolve over time, as new data may introduce biases. Periodic fairness audits should also be conducted, typically on an annual or semi-annual basis, to assess long-term fairness. Additionally, whenever there are significant changes, such as new data or model updates, fairness testing should be performed to ensure that no unintended biases have been introduced. In summary, fairness should be tested and monitored throughout the AI system’s lifecycle, from development to post-deployment, ensuring that the system remains equitable over time.