Does bias eventually increase with model complexity? Does bias eventually increase with model complexity?

Reasoning behind the question:
If I understand it correctly, "bias" measures the discrepancy between the expected value of our model's ($\hat{f}$) prediction and the expected value of the true, underlying model $f$. We can start by creating a simple model with high bias. Bias quickly drops as we train our model with the data since $\hat{f}$ starts to resemble the true $f$ function. However, our model may become too complex, too wiggly compared to the true model. Therefore introducing high bias again.
Edit:
I found a clue in An Introduction to Statistical Learning (2nd edition). FIGURE 2.10. on page 33 together with FIGURE 2.12. on page 36 visually suggest that the bias actually doesn't eventually increase with model complexity.

The plot on the left shows different models fitted to the data and the black line is a true relationship. The plot on the right corresponds to the data on the left.

It depends, is all I can say.
As a rule of thumb, bias decreases in a model as you add more parameters, but there can be some weird exceptions.
