  This work proposes to learn fair low-rank tensor decompositions by
regularizing the Canonical Polyadic Decomposition factorization with the kernel
Hilbert-Schmidt independence criterion (KHSIC). It is shown, theoretically and
empirically, that a small KHSIC between a latent factor and the sensitive
features guarantees approximate statistical parity. The proposed algorithm
surpasses the state-of-the-art algorithm, FATR (Zhu et al., 2018), in
controlling the trade-off between fairness and residual fit on synthetic and
real data sets.
