Stress-Testing ML Pipelines with Adversarial Data Corruption

2025-06-02

AI Summary

Real-world structured data often suffer from demographic missingness, biased labels, and systematic sampling bias—yet existing robustness evaluations rely on random or simplistic corruptions, failing to expose worst-case vulnerabilities of high-risk ML systems. Method: We propose SAVAGE, the first causality-driven, black-box interpretable stress-testing framework for structured data. It models data dependencies via causal graphs and implements corruption templates to enable causal representation of structured data contamination. Its novel bilevel optimization algorithm supports end-to-end, targeted vulnerability discovery—even for pipelines containing non-differentiable components. Results: Experiments show that just 5% contamination generated by SAVAGE induces catastrophic performance drops, significantly outperforming baselines. Moreover, SAVAGE reveals that core assumptions underlying mainstream data cleaning and fairness-aware learning methods systematically fail under realistic data defects.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Reasoning under Uncertainty: Causality
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

Structured data-quality issues, such as missing values correlated with demographics, culturally biased labels, or systemic selection biases, routinely degrade the reliability of machine-learning pipelines. Regulators now increasingly demand evidence that high-stakes systems can withstand these realistic, interdependent errors, yet current robustness evaluations typically use random or overly simplistic corruptions, leaving worst-case scenarios unexplored. We introduce SAVAGE, a causally inspired framework that (i) formally models realistic data-quality issues through dependency graphs and flexible corruption templates, and (ii) systematically discovers corruption patterns that maximally degrade a target performance metric. SAVAGE employs a bi-level optimization approach to efficiently identify vulnerable data subpopulations and fine-tune corruption severity, treating the full ML pipeline, including preprocessing and potentially non-differentiable models, as a black box. Extensive experiments across multiple datasets and ML tasks (data cleaning, fairness-aware learning, uncertainty quantification) demonstrate that even a small fraction (around 5 %) of structured corruptions identified by SAVAGE severely impacts model performance, far exceeding random or manually crafted errors, and invalidating core assumptions of existing techniques. Thus, SAVAGE provides a practical tool for rigorous pipeline stress-testing, a benchmark for evaluating robustness methods, and actionable guidance for designing more resilient data workflows.

Problem

• Addressing structured data-quality issues in ML pipelines
• Identifying worst-case corruption patterns affecting model performance
• Providing a practical tool for rigorous pipeline stress-testing

Innovation

• Causally inspired framework models data-quality issues
• Bi-level optimization identifies vulnerable data subpopulations
• Systematically discovers worst-case corruption patterns