The bias is an additive parameter in the convolution.
It’s like the b in f(x) = w*x + b. If you set bias=False, you will drop the b term, which might make sense in some cases, e.g. if the next layer is an affine BatchNorm layer.
Each kernel has an own bias term.
However, I think the concept is way better described in Stanford’s CS231n 600.