Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens

arXiv:2311.11463v2 Announce Type: replace
Abstract: After a machine learning (ML)-based system is deployed, monitoring its performance is important to ensure the safety and effectiveness of the algorithm over time. When an ML algorithm interacts with its environment, the algorithm can affect the data-generating mechanism and be a major source of bias when evaluating its standalone performance, an issue known as performativity. Although prior work has shown how to validate models in the presence of performativity using causal inference techniques, there …

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