  The detection of AT2017gfo proved that binary neutron star mergers are
progenitors of kilonovae. Using a combination of numerical-relativity and
radiative-transfer simulations, the community has developed sophisticated
models for these transients for a wide portion of the expected parameter space.
Using these simulations and surrogate models made from them, it has been
possible to perform Bayesian inference of the observed signals to infer
properties of the ejected matter. It has been pointed out that combining
inclination constraints derived from the kilonova with gravitational-wave
measurements increases the accuracy with which binary parameters can be
measured and allows a more accurate inference of the Hubble Constant. In order
to not introduce biases, constraints on the inclination angle for AT2017gfo
should be insensitive to the employed models. In this work, we compare
different assumptions about the ejecta and radiative reprocesses used by the
community and we investigate their impact on the parameter inference. While
most inferred parameters agree, we find disagreement between posteriors for the
inclination angle for different geometries that have been used in the
literature. According to our study, the inclusion of reprocessing of the
photons between different ejecta types improves the modeling fits to AT2017gfo
and in some cases affects the inferred constraints. Our study motivates the
inclusion of large $\sim$ 1 mag uncertainties in the kilonova models employed
for Bayesian analysis to capture yet unknown systematics, especially when
inferring inclination angles, although smaller uncertainties seem appropriate
to capture model systematics for other parameters. We also use this method to
impose soft constraints on the ejecta geometry of the kilonova AT2017gfo.
