Inconsistent Responsiveness Determination in Document Review: Difference of Opinion or Human Error?

This Article analyzes the inconsistency between different document review efforts on the same document collection to determine whether that inconsistency is due primarily to ambiguity in applying the definition of responsiveness to particular documents, or due primarily to human error. By examining documents from the TREC 2009 Legal Track, the Authors show that inconsistent assessments regarding the same documents are due in large part to human error. Therefore, the quality of a review effort is not simply a matter of opinion; it is possible to show objectively that some reviews, and some review methods, are better than others

Technology-Assisted Review in E-Discovery Can Be More Effective and More Efficient Than Exhaustive Manual Review

E-discovery processes that use automated tools to prioritize and select documents for review are typically regarded as potential cost-savers – but inferior alternatives – to exhaustive manual review, in which a cadre of reviewers assesses every document for responsiveness to a production request, and for privilege. This Article offers evidence that such technology-assisted processes, while indeed more efficient, can also yield results superior to those of exhaustive manual review, as measured by recall and precision, as well as F1, a summary measure combining both recall and precision. The evidence derives from an analysis of data collected from the TREC 2009 Legal Track Interactive Task, and shows that, at TREC 2009, technology-assisted review processes enabled two participating teams to achieve results superior to those that could have been achieved through a manual review of the entire document collection by the official TREC assessors

Artificial Intelligence as Evidence

This article explores issues that govern the admissibility of Artificial Intelligence (“AI”) applications in civil and criminal cases, from the perspective of a federal trial judge and two computer scientists, one of whom also is an experienced attorney. It provides a detailed yet intelligible discussion of what AI is and how it works, a history of its development, and a description of the wide variety of functions that it is designed to accomplish, stressing that AI applications are ubiquitous, both in the private and public sectors. Applications today include: health care, education, employment-related decision-making, finance, law enforcement, and the legal profession. The article underscores the importance of determining the validity of an AI application (i.e., how accurately the AI measures, classifies, or predicts what it is designed to), as well as its reliability (i.e., the consistency with which the AI produces accurate results when applied to the same or substantially similar circumstances), in deciding whether it should be admitted into evidence in civil and criminal cases. The article further discusses factors that can affect the validity and reliability of AI evidence, including bias of various types, “function creep,” lack of transparency and explainability, and the sufficiency of the objective testing of AI applications before they are released for public use. The article next provides an in-depth discussion of the evidentiary principles that govern whether AI evidence should be admitted in court cases, a topic which, at present, is not the subject of comprehensive analysis in decisional law. The focus of this discussion is on providing a step-by-step analysis of the most important issues, and the factors that affect decisions on whether to admit AI evidence. Finally, the article concludes with a discussion of practical suggestions intended to assist lawyers and judges as they are called upon to introduce, object to, or decide on whether to admit AI evidence

Solving non-uniqueness in agglomerative hierarchical clustering using multidendrograms

In agglomerative hierarchical clustering, pair-group methods suffer from a problem of non-uniqueness when two or more distances between different clusters coincide during the amalgamation process. The traditional approach for solving this drawback has been to take any arbitrary criterion in order to break ties between distances, which results in different hierarchical classifications depending on the criterion followed. In this article we propose a variable-group algorithm that consists in grouping more than two clusters at the same time when ties occur. We give a tree representation for the results of the algorithm, which we call a multidendrogram, as well as a generalization of the Lance and Williams' formula which enables the implementation of the algorithm in a recursive way.Comment: Free Software for Agglomerative Hierarchical Clustering using Multidendrograms available at http://deim.urv.cat/~sgomez/multidendrograms.ph