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US corporations regularly spend millions of dollars reviewingelectronically-stored documents in legal matters. Recently, attorneys applytext classification to efficiently cull massive volumes of data to identifyresponsive documents for use in these matters. While text classification isregularly used to reduce the discovery costs of legal matters, it also faces aperception challenge: amongst lawyers, this technology is sometimes looked uponas a "black box". Put simply, no extra information is provided for attorneys tounderstand why documents are classified as responsive. In recent years,explainable machine learning has emerged as an active research area. In anexplainable machine learning system, predictions or decisions made by a machinelearning model are human understandable. In legal 'document review' scenarios,a document is responsive, because one or more of its small text snippets aredeemed responsive. In these scenarios, if these responsive snippets can belocated, then attorneys could easily evaluate the model's documentclassification decisions - this is especially important in the field ofresponsible AI. Our prior research identified that predictive models createdusing annotated training text snippets improved the precision of a model whencompared to a model created using all of a set of documents' text as training.While interesting, manually annotating training text snippets is not generallypractical during a legal document review. However, small increases in precisioncan drastically decrease the cost of large document reviews. Automating theidentification of training text snippets without human review could then makethe application of training text snippet-based models a practical approach.

Explainable Text Classification Techniques in Legal Document Review: Locating Rationales without Using Human Annotated Training Text Snippets