  Accurate photometric redshift calibration is central to the robustness of all
cosmology constraints from cosmic shear surveys. Analyses of the KiDS
re-weighted training samples from all overlapping spectroscopic surveys to
provide a direct redshift calibration. Using self-organising maps (SOMs) we
demonstrate that this spectroscopic compilation is sufficiently complete for
KiDS, representing $99\%$ of the effective 2D cosmic shear sample. We use the
SOM to define a $100\%$ represented `gold' cosmic shear sample, per tomographic
bin. Using mock simulations of KiDS and the spectroscopic training set, we
estimate the uncertainty on the SOM redshift calibration, and find that
photometric noise, sample variance, and spectroscopic selection effects
(including redshift and magnitude incompleteness) induce a combined maximal
scatter on the bias of the redshift distribution reconstruction ($\Delta
\langle z \rangle=\langle z \rangle_{\rm est}-\langle z \rangle_{\rm true}$) of
$\sigma_{\Delta \langle z \rangle} \leq 0.006$ in all tomographic bins. We show
that the SOM calibration is unbiased in the cases of noiseless photometry and
perfectly representative spectroscopic datasets, as expected from theory. The
inclusion of both photometric noise and spectroscopic selection effects in our
mock data introduces a maximal bias of $\Delta \langle z \rangle
=0.013\pm0.006$, or $\Delta \langle z \rangle \leq 0.025$ at $97.5\%$
confidence, once quality flags have been applied to the SOM. The method
presented here represents a significant improvement over the previously adopted
direct redshift calibration implementation for KiDS, owing to its diagnostic
and quality assurance capabilities. The implementation of this method in future
cosmic shear studies will allow better diagnosis, examination, and mitigation
of systematic biases in photometric redshift calibration.
