<ul><li>Deploying machine learning models in safety-critical domains without access to ground truth labels poses challenges in ensuring reliable model performance on user data.</li><li>A novel framework called the suitability filter is proposed to detect performance deterioration by utilizing suitability signals that indicate potential prediction errors.</li><li>The suitability filter evaluates classifier accuracy on user data compared to a labeled test dataset, ensuring that any degradation stays within a predefined margin.</li><li>By aggregating suitability signals and using statistical hypothesis testing, the framework provides insights into decision uncertainty and reliably detects performance deviations due to covariate shift.</li></ul>

Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings

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