Opening the TAR Black Box: Developing an Interpretable System for eDiscovery Using the Fuzzy ARTMAP Neural Network
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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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 22 references that are in the map. First posted on arXiv 7 May 2023.
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References · 8
- 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
- 2015Autonomy and Reliability of Continuous Active Learning for Technology-Assisted Reviewcited by 13
- 2021Goldilocks: Just-Right Tuning of BERT for Technology-Assisted Reviewcited by 11
- 2019A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applicationscited by 10
- 2021On Minimizing Cost in Legal Document Review Workflowscited by 5
- 2019A Framework for Explainable Text Classification in Legal Document Reviewcited by 3
- 2019Empirical Evaluations of Active Learning Strategies in Legal Document Reviewcited by 2
- 2019Explainable Text Classification in Legal Document Review A Case Study of Explainable Predictive Codingcited by 2
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