Opening the TAR Black Box: Developing an Interpretable System for eDiscovery Using the Fuzzy ARTMAP Neural Network
This provides an explainable alternative to black-box TAR systems for legal professionals, though it is incremental as it builds on existing Fuzzy ARTMAP research.
The study tackled the problem of interpretability in Technology-Assisted Review (TAR) systems for eDiscovery by developing a system using the Fuzzy ARTMAP neural network, achieving robust recall results and demonstrating proof-of-concept If-Then rules for explainability.
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.