Bagging and bootstrap ensemble methods aren't really that confusing. Just think of it as stochastic gradient descent on a much larger hypothetical data set.

The effect is same one that occurs when you get a group of people together to estimate the number of jelly beans in a jar. All the estimators are biased, but if that bias is drawn from a zero mean distribution, deviation of the average bias goes down as the number of estimators increases.