# Towards Grad-CAM Based Explainability in a Legal Text Processing Pipeline

arXiv: https://arxiv.org/abs/2012.09603 · HTML: https://mlsanity.com/paper/2012.09603/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [Test Automation with Grad-CAM Heatmaps -- A Future Pipe Segment in MLOps for Vision AI?](https://mlsanity.com/paper/2103.01837/) (2021)
- [SenteCon: Leveraging Lexicons to Learn Human-Interpretable Language Representations](https://mlsanity.com/paper/2305.14728/) (2023)
- [EVolutionary Independent DEtermiNistiC Explanation](https://mlsanity.com/paper/2501.16357/) (2025)
- [Improving Industrial Injection Molding Processes with Explainable AI for Quality Classification](https://mlsanity.com/paper/2511.08108/) (2025)

## Abstract (arXiv)

Explainable AI(XAI)is a domain focused on providing interpretability and explainability of a decision-making process. In the domain of law, in addition to system and data transparency, it also requires the (legal-) decision-model transparency and the ability to understand the models inner working when arriving at the decision. This paper provides the first approaches to using a popular image processing technique, Grad-CAM, to showcase the explainability concept for legal texts. With the help of adapted Grad-CAM metrics, we show the interplay between the choice of embeddings, its consideration of contextual information, and their effect on downstream processing.