Evaluating Black-Box Vulnerabilities with Wasserstein-Constrained Data Perturbations
This addresses the challenge of explainability and robustness in black-box ML models for industry applications, but appears incremental as it applies existing theory to a known bottleneck.
The paper tackles the problem of evaluating vulnerabilities in black-box machine learning models by using Optimal Transport theory to find the closest distribution under Wasserstein constraints and analyzing its impact on model behavior, with convergence results and demonstrations on real-world datasets.
The massive use of Machine Learning (ML) tools in industry comes with critical challenges, such as the lack of explainable models and the use of black-box algorithms. We address this issue by applying Optimal Transport theory in the analysis of responses of ML models to variations in the distribution of input variables. We find the closest distribution, in the Wasserstein sense, that satisfies a given constraintt and examine its impact on model behavior. Furthermore, we establish convergence results for this projected distribution and demonstrate our approach using examples and real-world datasets in both regression and classification settings.