Comparison of Tools and Methods for Technology-Assisted Review

In a large-scale eDiscovery effort in Irish litigation, human assessors participated in two technology-assisted reviews (“TAR”) employing continuous active learning (“CAL”) processes, one using Grossman and Cormack’s logistic regression CAL tool and the other using a leading eDiscovery provider’s support-vector-machine-based (“SVM”) tool. In this work, we investigate the extent to which the different tools and associated methods impacted the effectiveness and efficiency of the competing TAR reviews across the same document population, measured by recall, precision, review effort, and the average cost incurred per relevant document found. Our results show that the tool and method underlying the TAR model matters – the CAL process outperformed the provider’s process on all measures.