Model bias is not a huge issue. Maybe something about class imbalance or regularization. The huge issue is deployment - what is the model used for? How is it affecting people in reality? What metric is it optimizing?

Between all these the degree of L1 regularization or the class weights are minor things. Most models will perform similarly given the same data. It's mostly the data that makes the difference.

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.

Citations needed to back your claim. Research literature seem to support the opposite.

Is that your personal opinion? Research seems to disagree. Citations in the previous link.

Hyperparameters play a significant role in bias when you're dealing with imbalanced classes, or long tail samples.

But this ties back to the original data problem, right? If you don't have enough training samples for (known or unknown) unknowns, your model is likely to be biased against them.

While this is true, "learning the biases of the researchers who built it" is a very misleading way of putting it, because it is still very unclear if and how certain design decisions impact the bias of the resulting model.

Given that reducing bias while not giving up other desirable properties is a young and open research direction, researchers in general should not be faulted for using the current (imperfect) state of the art or for working on something that is not (yet) focused on bias.

https://doxa.substack.com/p/googles-colosseum is a pretty good treatment of this topic.

Yes, the optimization goal (the objective function) is a major factor in the function of algorithmic systems. I'm not sure bias is the best word to use here, however.

It is a known challenge to align the designed purpose of an algorithm with actual optimization metrics. For instance, recommendation systems may have the purpose of improving user experience, but if time-on-site metrics are used as the optimization function, there can be unexpected results.