Predictive modeling - known in the legal industry as ‘predictive coding’ or ‘Technology Assisted Review (TAR)’ - is a popular tool used by legal professionals to augment a historically manual document review and classification process in responding to data requests for various legal proceedings. It has become more commonplace over the past decade due to its proven ability to minimize manual document classification, thus reducing the time and cost associated with this aspect of legal proceedings. There is significant research supporting the effectiveness of this technology that includes topics, such as identifying the most performant machine learning algorithms (e.g., logistic regression) or establishing the best methodologies for selecting representative training and testing data. Primarily, this research has been performed without a focus on multilanguage data sets because many legal proceedings typically involve one primary, dominant language. As acceptance of predictive modeling technology grows, legal practitioners have developed independent preferences for handling multiple languages in data sets. These preferences have been primarily based on anecdotal experience rather than empirical assessments and have resulted in two modeling approaches. The first approach uses a single model, which is less complex, and more cost effective than the next approach. The second develops multiple language-specific models, which can create workflow complexity that may impact the overall cost savings that predictive coding seeks to achieve. Proponents of the second approach believe that creating models per language results in better performance over a single model approach. In this study, we empirically explore the performance differences between the two approaches - single model and multiple language-specific models. We hypothesize that a single model approach performs similarly to a language-specific modeling approach when classifying documents for relevance. We evaluate both approaches by comparing their resulting precision-recall curves across real-world test data from legal document review projects. Excitingly, our results demonstrate that in most scenarios, a single model (trained with multiple languages) approach will perform as well, or sometimes better than the approach that uses a group of language-specific models. The results of our research provide a roadmap for legal practitioners and other parties to thoughtfully engage in dialogue around the most effective way to deploy predictive coding within a multilanguage data set. This research will change how predictive models are deployed for multilanguage data sets across the legal industry and enable significant cost savings for clients and legal practitioners by utilizing the most efficient modeling method.

US corporations regularly spend millions of dollars reviewing electronically-stored documents in legal matters. Recently, attorneys apply text classification to efficiently cull massive volumes of data to identify responsive documents for use in these matters. While text classification is regularly used to reduce the discovery costs of legal matters, it also faces a perception challenge: amongst lawyers, this technology is sometimes looked upon as a "black box." Put simply, no extra information is provided for attorneys to understand why documents are classified as responsive. In recent years, explainable machine learning has emerged as an active research area. In an explainable machine learning system, predictions or decisions made by a machine learning model are human understandable. In legal ‘document review’ scenarios, a document is responsive, because one or more of its small text snippets are deemed responsive. In these scenarios, if these responsive snippets can be located, then attorneys could easily evaluate the model’s document classification decisions – this is especially important in the field of responsible AI. Our prior research identified that predictive models created using annotated training text snippets improved the precision of a model when compared to a model created using all of a set of documents’ text as training. While interesting, manually annotating training text snippets is not generally practical during a legal document review. However, small increases in precision can drastically decrease the cost of large document reviews. Automating the identification of training text snippets without human review could then make the application of training text snippet-based models a practical approach. This paper proposes two simple machine learning methods to locate responsive text snippets within responsive documents without using human annotated training text snippets. The two methods were evaluated and compared with a document classification method using three datasets from actual legal matters. The results show that the two proposed methods outperform the document-level training classification method in identifying responsive text snippets in responsive documents. Additionally, the results suggest that we can automate the successful identification of training text snippets to improve the precision of our predictive models in legal document review and thereby help reduce the overall cost of review.

In eDiscovery, it is critical to ensure that each page produced in legal proceedings conforms with the requirements of court or government agency production requests. Errors in productions could have severe consequences in a case, putting a party in an adverse position. The volume of pages produced continues to increase, and tremendous time and effort has been taken to ensure quality control of document productions. This has historically been a manual and laborious process. This paper demonstrates a novel automated production quality control application which leverages deep learning-based image recognition technology to extract Bates Number and Confidentiality Stamping from legal case production images and validate their correctness. Effectiveness of the method is verified with an experiment using a real-world production data.

Companies regularly spend millions of dollars producing electronically-stored documents in legal matters. Over the past two decades, attorneys have been using a variety of technologies to conduct this exercise, and most recently, parties on both sides of the `legal aisle' are accepting the use of machine learning techniques like text classification to cull massive volumes of data and to identify responsive documents for use in these matters. While text classification is regularly used to reduce the discovery costs in legal matters, text classification also faces a peculiar perception challenge: amongst lawyers, this technology is sometimes looked upon as a black box Put simply, very little information is provided for attorneys to understand why documents are classified as responsive. In recent years, a group of AI and Machine Learning researchers have been actively researching Explainable AI. In an explainable AI system, actions or decisions are human understandable. In legal `document review' scenarios, a document can be identified as responsive, as long as one or more of the text snippets (small passages of text) in a document are deemed responsive. In these scenarios, if text classification can be used to locate these responsive snippets, then attorneys could easily evaluate the model's document classification decision. When deployed with defined and explainable results, text classification can drastically enhance the overall quality and speed of the document review process by reducing the time it takes to review documents. Moreover, explainable predictive coding provides lawyers with greater confidence in the results of that supervised learning task. This paper describes a framework for explainable text classification as a valuable tool in legal services: for enhancing the quality and efficiency of legal document review and for assisting in locating responsive snippets within responsive documents. This framework has been implemented in our legal analytics product, which has been used in hundreds of legal matters. We also report our experimental results using the data from an actual legal matter that used this type of document review.

Training documents have a significant impact on the performance of predictive models in the legal domain. Yet, there is limited research that explores the effectiveness of the training document selection strategy - in particular, the strategy used to select the seed set, or the set of documents an attorney reviews first to establish an initial model. Since there is limited research on this important component of predictive coding, the authors of this paper set out to identify strategies that consistently perform well. Our research demonstrated that the seed set selection strategy can have a significant impact on the precision of a predictive model. Enabling attorneys with the results of this study will allow them to initiate the most effective predictive modeling process to comb through the terabytes of data typically present in modern litigation. This study used documents from four actual legal cases to evaluate eight different seed set selection strategies. Attorneys can use the results contained within this paper to enhance their approach to predictive coding.