Before revision:  In some cases, even worst-case splits  under-estimate the error observed on new samples of in-domain data, i.e., the data that models should minimally generalize to at test time.
After revision:  In some cases, even worst-case splits , maximizing bias, often under-estimate the error observed on new samples of in-domain data, i.e., the data that models should minimally generalize to at test time.