The purpose of this study is to extract unethical use cases from the Korean dictionary to determine whether they are ethical and to find the right way to present ethical use cases. In this study, the problem of misclassification and undetected ethical automatic classifier of the Korean dictionary example was analyzed through the crowd worker and the ethical automatic classifier ‘Safety Check(Korv0.5). This study argued that cases with long-established unethical bias should be reviewed again through unethical cases in the Korean dictionary. Ethical information should be structured according to the purpose of the dictionary, but unethical use cases should be given additional attachment information, or in the case of new words, ethical regulations are needed in the guidelines for preparing examples. The unethical case analysis data reviewed in this study were disclosed to help improve the model through balanced data learning in subsequent studies.