In this paper we discuss pre- and post-processing methods to induce desired consistency and/or invariance properties in blackbox systems, e.g., AI-based. We demonstrate our approach in the context of blackbox SVD-based matrix-completion methods commonly used in recommender system (RS) applications. We provide empirical results showing that enforcement of unit-consistency and shift-consistency, which have provable RS-relevant properties relating to robustness and fairness, also lead to improved performance according to generic RMSE and MAE performance metrics, irrespective of the initial chosen hyperparameter.
@article{arxiv.2307.08760,
title = {Imposing Consistency Properties on Blackbox Systems with Applications to SVD-Based Recommender Systems},
author = {Tung Nguyen and Jeffrey Uhlmann},
journal= {arXiv preprint arXiv:2307.08760},
year = {2023}
}