Bias is the characteristic of models to learn from some variables and not others. Some bias is essential, since machine learning must predict based on data features that are more predictive than others.
High bias occurs when model training uses too few variables, due either to limited training data features or restrictions on the number of variables and algorithm is able to consider. High bias results in underfitting.
Low bias desirable, but is a trade-off with variance in algorithm performance.