Covariance-Robust Minimax Probability Machines for Algorithmic Recourse

Ngoc Bui, Duy Nguyen, Kim-Cuc Nguyen, Man-Chung Yue, Viet Anh Nguyen

Keywords: Optimization and Learning under Uncertainty, Algorithmic Recourse, Trustworthy ML and Statistics

TL;DR: We propose a novel pipeline to generate a model-agnostic recourse that is robust to model shifts.

Abstract: Algorithmic recourse is rising as a prominent technique to promote the explainability and transparency of the predictive model in ethical machine learning. Existing approaches to algorithmic recourse often assume an invariant predictive model; however, this model, in reality, is usually updated temporally upon the input of new data. Thus, a recourse that is valid respective to the present model may become invalid for the future model. To resolve this issue, we propose a pipeline to generate a model-agnostic recourse that is robust to model shifts. Our pipeline first estimates a linear surrogate of the nonlinear (black-box) model using covariance-robust minimax probability machines (MPM); then, the recourse is generated with respect to this robust linear surrogate. We show that the covariance-robust MPM recovers popular regularization schemes, including l_2-regularization and class-reweighting. We also show that our covariance-robust MPM pushes the decision boundary in an intuitive manner, which facilitates an interpretable generation of a robust recourse. The numerical results demonstrate the usefulness and robustness of our pipeline.

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