  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.
