# Opening the TAR Black Box: Developing an Interpretable System for eDiscovery Using the Fuzzy ARTMAP Neural Network

arXiv: https://arxiv.org/abs/2305.04237 · HTML: https://mlsanity.com/paper/2305.04237/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 7 May 2023.

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

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Autonomy and Reliability of Continuous Active Learning for Technology-Assisted Review](https://mlsanity.com/paper/1504.06868/) (2015)
- [Goldilocks: Just-Right Tuning of BERT for Technology-Assisted Review](https://mlsanity.com/paper/2105.01044/) (2021)
- [A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications](https://mlsanity.com/paper/1905.11437/) (2019)
- [On Minimizing Cost in Legal Document Review Workflows](https://mlsanity.com/paper/2106.09866/) (2021)
- [A Framework for Explainable Text Classification in Legal Document Review](https://mlsanity.com/paper/1912.09501/) (2019)
- [Empirical Evaluations of Active Learning Strategies in Legal Document Review](https://mlsanity.com/paper/1904.01719/) (2019)
- [Explainable Text Classification in Legal Document Review A Case Study of Explainable Predictive Coding](https://mlsanity.com/paper/1904.01721/) (2019)

## Abstract (arXiv)

This foundational research provides additional support for using the Fuzzy ARTMAP neural network as a classification algorithm in the TAR domain. While research opportunities exist to improve recall performance and explanation, the robust recall results from this study and the proof-of-concept demonstration of If-Then rules for tf-idf vectorization strongly substantiate that a Fuzzy ARTMAP-based TAR system is a potentially viable explainable alternative to "black box" TAR systems.