"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift (2506.00756v1)

Abstract: Machine learning (ML) models frequently experience performance degradation when deployed in new contexts. Such degradation is rarely uniform: some subgroups may suffer large performance decay while others may not. Understanding where and how large differences in performance arise is critical for designing targeted corrective actions that mitigate decay for the most affected subgroups while minimizing any unintended effects. Current approaches do not provide such detailed insight, as they either (i) explain how average performance shifts arise or (ii) identify adversely affected subgroups without insight into how this occurred. To this end, we introduce a Subgroup-scanning Hierarchical Inference Framework for performance drifT (SHIFT). SHIFT first asks "Is there any subgroup with unacceptably large performance decay due to covariate/outcome shifts?" (Where?) and, if so, dives deeper to ask "Can we explain this using more detailed variable(subset)-specific shifts?" (How?). In real-world experiments, we find that SHIFT identifies interpretable subgroups affected by performance decay, and suggests targeted actions that effectively mitigate the decay.

• The paper introduces SHIFT, a hierarchical framework using two-stage hypothesis testing to diagnose non-uniform performance decay in ML models across specific subgroups and identify contributing variables.
• SHIFT is designed to be robust and applicable even with limited data, detecting distribution shifts that cause significant performance changes based on expert-defined thresholds.
• Numerical results and case studies demonstrate SHIFT's effectiveness in identifying subgroups with performance drifts, offering insights for targeted fairness interventions and model corrections.

A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift

Understanding the heterogeneity in performance drift of machine learning models across different domains is critical for ensuring their reliability, fairness, and robustness. Performance degradation is frequently observed when models are deployed in contexts different from their training environment, but this degradation is not uniformly experienced across all data subgroups. Some subgroups might experience severe performance decay while others remain relatively unaffected. The paper by Singh et al. tackles this non-uniform model decay with a novel inferential framework termed SHIFT (Subgroup-scanning Hierarchical Inference Framework for performance drifT). Their methodology allows for the identification of subgroups experiencing significant performance decay and attributes these decays to specific distribution shifts.

Technical Innovations

The core innovation presented is the formulation of SHIFT as a two-stage hypothesis testing framework tailored to diagnose performance drift. Initially, SHIFT detects whether any subgroup experiences large decay due to aggregate covariate and outcome shifts. Upon detection, it further drills down to identify specific variables that may account for these shifts within affected subgroups. This hierarchical framework leverages hypothesis testing to provide statistical guarantees, ensuring controlled Type I error rates even when dealing with shift heterogeneities.

An important feature of SHIFT is its applicability in settings with limited data, providing robust subgroup-level insights without requiring detailed causal graphs or large datasets. It focuses on detecting distribution shifts that result in meaningful performance changes, setting thresholds in terms of subgroup size and decay magnitude, specified by domain experts.

Numerical Results and Comparisons

Through simulations and real-world case studies, SHIFT is demonstrated to identify relevant distribution shifts effectively. For instance, in healthcare and demographic data applications, SHIFT successfully pinpointed subgroups with significant performance drifts due to both covariate and outcome shifts. Comparisons with baseline methods such as Kernel Conditional Independence (KCI) and Maximum Mean Discrepancy (MMD) illustrate SHIFT’s superior power and precision in identifying non-uniform performance shifts.

Practical and Theoretical Implications

The framework's ability to dissect the root causes of performance shifts has substantial implications for model fairness and deployment. By identifying which subgroups and features contribute to performance variances, adjustments can be made selectively, thus preserving performance where it is already satisfactory ("Don't fix what ain't broke"). The analyses presented highlight SHIFT's utility in designing targeted corrective actions and furthering our understanding of algorithmic fairness by focusing on subgroup performance disparities.

Moreover, the theoretical contributions include novel test formulations and proofs ensuring Type I error control when testing for performance decay across subgroups. This methodological approach is not only applicable to the scenarios presented but serves as a foundation for future research in subgroup-level model diagnostics and intervention strategies.

Speculations on Future Developments

In the field of AI and ML, personalized interventions based on subgroup-specific diagnostics might become a norm. SHIFT paves the way for deeper integration of fairness and robustness into AI systems, suggesting paths for the development of more nuanced alignment strategies between model objectives and real-world applications. Future work may extend SHIFT to accommodate complex multivariate shifts and integrate causal inference tools for finer subgroup analysis.

Overall, the framework presents a sophisticated approach to diagnosing performance drift, moving the needle forward in terms of both theoretical depth and practical applicability. As AI continues to permeate different domains, tools like SHIFT will be pivotal in assuring that these technologies operate equitably across diverse populations.