Interpretable machine learning models offer understandable reasoning behind their decision-making process, though they may not always match the performance of their black-box counterparts. This trade-off between interpretability and model performance has sparked discussions around the deployment of AI, particularly in critical applications where knowing the rationale of decision-making is essential for trust and accountability. In this study, we conduct a comparative analysis of several black-box and interpretable models, focusing on a specific NLP use case that has received limited attention: inferring ratings from reviews. Through this use case, we explore the intricate relationship between the performance and interpretability of different models. We introduce a quantitative score called Composite Interpretability (CI) to help visualize the trade-off between interpretability and performance, particularly in the case of composite models. Our results indicate that, in general, the learning performance improves as interpretability decreases, but this relationship is not strictly monotonic, and there are instances where interpretable models are more advantageous.

本研究解决了可解释性和模型性能之间的权衡问题，特别是在推断评论评分的自然语言处理应用中。我们引入了一种定量评分方法，称为综合可解释性（CI），以帮助可视化不同模型的可解释性与性能之间的关系。研究发现，虽然一般来说，随着可解释性的降低，学习性能有所提高，但这种关系并非严格单调，某些情况下可解释模型更具优势。

揭示准确性与可解释性之间的权衡：从评论推断评分的案例研究