Monitoring Strategy

Monitoring strategies for deployed systems, particularly machine learning models, aim to ensure ongoing performance and safety by detecting anomalies and biases. Current research emphasizes the development of efficient and interpretable monitoring methods, often incorporating causal inference techniques to account for system performativity and employing algorithms like collaborative learning and quantile-based anomaly detection. These advancements are crucial for improving the reliability and trustworthiness of complex systems across diverse applications, from healthcare to industrial processes, by enabling timely intervention and reducing risks associated with unforeseen failures or biases.