This paper proposes a new method for training debiased classifiers with no bias supervision. The key idea of the method is to employ a committee of classifiers as an auxiliary module that identifies bias-conflicting data and assigns large weights to them when training the main classifier. The committee is learned as a bootstrapped ensemble so that a majority of its classifiers are biased as well as being diverse, and intentionally fail to predict classes of bias-conflicting data accordingly. The consensus within the committee on prediction difficulty provides a reliable cue for identifying and weighting bias-conflicting data. Moreover, the committee is trained also with knowledge transferred from the main classifier so that it gradually becomes debiased and emphasizes more difficult data as training progresses.On five real-world datasets, our method outperforms previous arts using no bias label like ours and even surpasses those relying on bias labels occasionally.