  The spins of black holes in merging binaries can reveal information related
to the formation and evolution of these systems through their gravitational
wave emission. Combining events to infer the astrophysical distribution of
black hole spins allows us to determine the relative contribution from
different formation scenarios to the population. Many previous works have
modeled spin population distributions using parametric models. While these are
valuable approaches when the observed population is small, they make strong
assumptions about the shape of the underlying distribution and are highly
susceptible to biases due to mismodeling. The results obtained with such
parametric models are only valid if the allowed shape of the distribution is
well-motivated (i.e. for astrophysical reasons). In this work, we relax these
prior assumptions and model the spin distributions using a more data-driven
approach, modeling these distributions with flexible cubic spline interpolants
in order to allow for capturing structures that the parametric models cannot.
We find that adding this flexibility to the model substantially increases the
uncertainty in the inferred distributions, but find a general trend for lower
support at high spin magnitude and a spin tilt distribution consistent with
isotropic orientations. We infer that 62 - 87% of black holes have spin
magnitudes less than a = 0.5, and 27- 50% of black holes exhibit negative
$\chi_{\rm eff}$. Using the inferred $\chi_{\rm eff}$ distribution, we place a
conservative upper limit of 37% for the contribution of hierarchical mergers to
the astrophysical BBH population. Additionally, we find that artifacts from
unconverged Monte Carlo integrals in the likelihood can manifest as spurious
peaks and structures in inferred distributions, mandating the use of a
sufficient number of samples when using Monte Carlo integration for population
inference.
