Why bias is not considering in Regularization?

Imagine you have a bunch of points that lie roughly on the line y=x. Although you could find a polynomial that passes through each and every single point you could argue that the line y=x is a better approximator because it doesn't fit to the noise of each point.

That is the point of regularization. When you have a network with smaller weights a few small differences don't make much of an impact therefore you make it harder to learn noise in your data. The difference with biases is that they don't affect a neuron in the same way as a weight. The bias acts as more like a threshold value, where after collecting the result from the weights and previous activations you see if it matters enough to make the neuron become positive or not. For this reason, you only focus on weights in regularization. Hope this helps!

The point of regularization is to avoid overfitting, and overfitting happens when you have too many predictor variables (i.e. neurons) contributing to the outcome. So, by regularization you are essentially excluding some neurons.

However, bias is an intrinsic property of every neuron. It is not a connection transporting the result of a neuron from the previous layer. Maybe you can imagine all biases in the network being connections to a single, virtual neuron with a constant output of one, but still, as it is a constant, it is not a result of a computation and does not depend on the input.