There's an interplay between the two insofar as a model built to handle a specific dataset will involve design decisions informed by the data. E.g. you might pick a certain level of L1 regularization because it maximizes performance on the data you have, which can lead to bias against data you don't have.
But if you take "model" to mean the pure mathematical description without parameters or hyperparameters that need to be determined by experimentation, then I agree that optimizing the model on a dataset will not lead to bias against specific groups of humans unless the data used contains such a bias.
But if you take "model" to mean the pure mathematical description without parameters or hyperparameters that need to be determined by experimentation, then I agree that optimizing the model on a dataset will not lead to bias against specific groups of humans unless the data used contains such a bias.