Kernel and neural network (NN) based machine-learning (ML) methods have in recent years become established as an essential addition to the toolbox of computational chemistry. In particular, ML-based interatomic potentials have had great success in providing energies and forces with quantum mechanical accuracy at a fraction of the cost of first-principles calculations. To achieve size-extensivity and a linear computational scaling with system size, these ML potentials typically rely on a local representation of atomic environments and consequently assume that the energy can be decomposed into local atomic contributions. This simple idea has led to a strong focus of chemical ML research on developing sophisticated representations of local atomic environments and, relatedly, NN architectures that directly embed atoms in their neighborhood. At the same time, it is clear that the assumption of locality does not hold for all systems to the same extent. Indeed, strongly polar or ionic systems display very long-ranged Coulomb interactions. Even for a fairly unpolar (e.g organic) system, the locality of the energy does not necessarily imply that other electronic properties are similarly local. In particular, electronic properties such as molecular orbital energies or dipole moments can break locality assumptions. Consequently, such properties tend to be more challenging to predict with purely data-driven ML methods. Beyond this methodological challenge, dipole moments are an intrinsically interesting target as they govern the asymptotic decay of interactions of neutral molecules and their absorption cross-sections in vibrational spectroscopy.