  Although photometric redshifts (photo-z's) are crucial ingredients for
current and upcoming large-scale surveys, the high-quality spectroscopic
redshifts currently available to train, validate, and test them are
substantially non-representative in both magnitude and color. We investigate
the nature and structure of this bias by tracking how objects from a
heterogeneous training sample contribute to photo-z predictions as a function
of magnitude and color, and illustrate that the underlying redshift
distribution at fixed color can evolve strongly as a function of magnitude. We
then test the robustness of the galaxy-galaxy lensing signal in 120 deg$^2$ of
HSC-SSP DR1 data to spectroscopic completeness and photo-z biases, and find
that their impacts are sub-dominant to current statistical uncertainties. Our
methodology provides a framework to investigate how spectroscopic
incompleteness can impact photo-z-based weak lensing predictions in future
surveys such as LSST and WFIRST.
