Evaluating interpretability of state-of-the-art NLP models for predicting moral values

Abstract

Understanding personal values is a crucial aspect that can facilitate the collaboration between AI and humans. Nonetheless, the implementation of collaborative agents in real life greatly depends on the amount of trust that is built in their relationship with people. In order to bridge this gap, more extensive analysis of the explainability of these systems needs to be conducted. We implement LSTM, BERT and FastText, three deep learning models for text classification and compare their interpretability on the task of predicting moral values from opinionated text. The results highlight the different degrees to which the behaviour of the three models can be explained in the context of moral value prediction. Our experiments showed that BERT, current state-of-the-art in natural language processing tasks, achieves the best performance while also providing more interpretable predictions than the other two models.