  The increasing statistical precision of photometric redshift surveys requires
improved accuracy of theoretical predictions for large-scale structure
observables to obtain unbiased cosmological constraints. In $\Lambda$CDM
cosmologies, massive neutrinos stream freely at small cosmological scales,
suppressing the small-scale power spectrum. In massive neutrino cosmologies,
galaxy bias modeling needs to accurately relate the scale-dependent growth of
the underlying matter field to observed galaxy clustering statistics. In this
work, we implement a computationally efficient approximation of the
neutrino-induced scale-dependent bias (NISDB). Through simulated likelihood
analyses of Dark Energy Survey Year 3 (DESY3) and Legacy Survey of Space and
Time Year 1 (LSSTY1) synthetic data that contain an appreciable NISDB, we
examine the impact of linear galaxy bias and neutrino mass modeling choices on
cosmological parameter inference. We find model misspecification of the NISDB
approximation and neutrino mass models to decrease the constraining power of
photometric galaxy surveys and cause parameter biases in the cosmological
interpretation of future surveys. We quantify these biases and devise
mitigation strategies.
