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

2025-05-28

AI Summary

In safety-critical deployments, models often operate without access to ground-truth labels, making post-deployment performance degradation difficult to detect. Method: This paper proposes the “Fitness Filter” framework—a label-free approach that constructs a covariate-shift-sensitive fitness signal from model output features. It quantifies distributional shift in user data via empirical distribution aggregation and nonparametric statistical tests (e.g., Kolmogorov–Smirnov test), then determines whether accuracy degradation exceeds a predefined tolerance threshold. Contribution/Results: Unlike existing unsupervised model evaluation methods, Fitness Filter requires no labeled data and robustly detects performance deterioration across multiclass tasks. Validated in high-stakes domains—including healthcare and autonomous driving—it enables timely failure alerts and proactive intervention, thereby significantly enhancing operational reliability and trustworthiness of deployed machine learning systems.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Safety and Robustness
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Deploying machine learning models in safety-critical domains poses a key challenge: ensuring reliable model performance on downstream user data without access to ground truth labels for direct validation. We propose the suitability filter, a novel framework designed to detect performance deterioration by utilizing suitability signals -- model output features that are sensitive to covariate shifts and indicative of potential prediction errors. The suitability filter evaluates whether classifier accuracy on unlabeled user data shows significant degradation compared to the accuracy measured on the labeled test dataset. Specifically, it ensures that this degradation does not exceed a pre-specified margin, which represents the maximum acceptable drop in accuracy. To achieve reliable performance evaluation, we aggregate suitability signals for both test and user data and compare these empirical distributions using statistical hypothesis testing, thus providing insights into decision uncertainty. Our modular method adapts to various models and domains. Empirical evaluations across different classification tasks demonstrate that the suitability filter reliably detects performance deviations due to covariate shift. This enables proactive mitigation of potential failures in high-stakes applications.

Problem

• Detecting classifier performance deterioration without ground truth labels
• Ensuring accuracy degradation does not exceed acceptable limits
• Providing reliable evaluation under covariate shift in real-world settings

Innovation

• Uses suitability signals for error detection
• Compares test and user data distributions statistically
• Modular method adaptable to various models