For order $q$ kernel density estimators we show that the constant $b_q$ in $bias=b_qh^q+o(h^q)$ can be made arbitrarily small, while keeping the variance bounded. A data-based selection of bq is presented and Monte Carlo simulations illustrate the advantages of the method.