# Demystifying the Accuracy-Interpretability Trade-Off: A Case Study of Inferring Ratings from Reviews

arXiv: https://arxiv.org/abs/2503.07914 · HTML: https://mlsanity.com/paper/2503.07914/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 10 Mar 2025.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model](https://mlsanity.com/paper/1511.01644/) (2015)
- [Robust Counterfactual Explanations for Tree-Based Ensembles](https://mlsanity.com/paper/2207.02739/) (2022)
- [Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees](https://mlsanity.com/paper/2305.11997/) (2023)
- [A Survey on the Robustness of Feature Importance and Counterfactual Explanations](https://mlsanity.com/paper/2111.00358/) (2021)

## Built on by (most cited first, 5 of 5)

- [Holistic Explainable AI (H-XAI): Extending Transparency Beyond Developers in AI-Driven Decision Making](https://mlsanity.com/paper/2508.05792/) (2025)
- [Vision-Based Embedded System for Noncontact Monitoring of Preterm Infant Behavior in Low-Resource Care Settings](https://mlsanity.com/paper/2509.02018/) (2025)
- [A Gap Between Decision Trees and Neural Networks](https://mlsanity.com/paper/2601.03919/) (2026)
- [The Confusion is Real: GRAPHIC -- A Network Science Approach to Confusion Matrices in Deep Learning](https://mlsanity.com/paper/2602.19770/) (2026)
- [WeNLEX: Weakly Supervised Natural Language Explanations for Multilabel Chest X-ray Classification](https://mlsanity.com/paper/2603.18752/) (2026)

## Abstract (arXiv)

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.