How to Use Litigation Technology to Prepare & Present Your Case at Trial October 27, 2021

Meeting proceedings of a seminar by the same name, held October 27, 2021

Natural Language Processing and Machine Learning as Practical Toolsets for Archival Processing

Peer ReviewedPurpose – This study aims to provide an overview of recent efforts relating to natural language processing (NLP) and machine learning applied to archival processing, particularly appraisal and sensitivity reviews, and propose functional requirements and workflow considerations for transitioning from experimental to operational use of these tools. Design/methodology/approach – The paper has four main sections. 1) A short overview of the NLP and machine learning concepts referenced in the paper. 2) A review of the literature reporting on NLP and machine learning applied to archival processes. 3) An overview and commentary on key existing and developing tools that use NLP or machine learning techniques for archives. 4) This review and analysis will inform a discussion of functional requirements and workflow considerations for NLP and machine learning tools for archival processing. Findings – Applications for processing e-mail have received the most attention so far, although most initiatives have been experimental or project based. It now seems feasible to branch out to develop more generalized tools for born-digital, unstructured records. Effective NLP and machine learning tools for archival processing should be usable, interoperable, flexible, iterative and configurable. Originality/value – Most implementations of NLP for archives have been experimental or project based. The main exception that has moved into production is ePADD, which includes robust NLP features through its named entity recognition module. This paper takes a broader view, assessing the prospects and possible directions for integrating NLP tools and techniques into archival workflows

SENTIMENT AND BEHAVIORAL ANALYSIS IN EDISCOVERY

A suspect or person-of-interest during legal case review or forensic evidence review can exhibit signs of their individual personality through the digital evidence collected for the case. Such personality traits of interest can be analytically harvested for case investigators or case reviewers. However, manual review of evidence for such flags can take time and contribute to increased costs. This study focuses on certain use-case scenarios of behavior and sentiment analysis as a critical requirement for a legal case’s success. This study aims to quicken the review and analysis phase and offers a software prototype as a proof-of-concept. The study starts with the build and storage of Electronic Stored Information (ESI) datasets for three separate fictitious legal cases using publicly available data such as emails, Facebook posts, tweets, text messages and a few custom MS Word documents. The next step of this study leverages statistical algorithms and automation to propose approaches towards identifying human sentiments, behavior such as, evidence of financial fraud behavior, and evidence of sexual harassment behavior of a suspect or person-of-interest from the case ESI. The last stage of the study automates these approaches via a custom software and presents a user interface for eDiscovery teams and digital forensic investigators

Challenges and Opportunities in using Analytics Combined with Visualisation Techniques for Finding Anomalies in Digital Communications

Digital communication has changed human life since the invention of the internet. The growth of E-mail, social websites and other interpersonal communication systems in turn have brought rapid development in especially the key technological area of data analytics. Using advanced forms of analytics helps the examination of data and better informs investigative sense-making and decision-making of all kinds. The legal process called Electronic discovery (E-discovery) is used for investigating various events in the digital communication world, for the purpose of producing/obtaining evidence (such as evidence in the form of emails used in the Enron fraud case). Investigating digital communications collected over a period of time, manually, is a strenuous process, time consuming, expensive and not very effective. More recently, within E-discovery there has been development of analytics known in the legal community as “Technology assisted review” (TAR). TAR is a technologydriven assistant in E-discovery for identifying relevance in the documents/data which saves time and improves efficiency in investigation. At the same time, the efficacy of visualisation tools currently available in the market is increasing, where such tools depend on a combination of simple keyword searches and more complex representations (e.g. network graphs). Also in E-discovery, early case assessment is a process of estimating risk (cost and time) to prosecute or defend a legal case based on an early review of potentially relevant electronically stored information (ESI). Legal firms largely determine the duration of the E-discovery process and charge companies based on the volume of information collected and reviewed after an automated search, where ESI may then be manually reviewed intensely to determine relevance and privilege. This results in significant costs for the company or in a number of cases settlement because a party cannot afford to continue with the lawsuit due to Ediscovery costs. This paper examines some of the opportunities and challenges in searching digital communication data for E-discovery and investigations, and will explore how analytics coupled with visualisation techniques may lend support and guidance in these efforts. Addressing these combined techniques may yet yield improved data collection, analysis and understanding of how analysts/lawyers can work together using visualisations. In particular, we attempt to address two challenges: (i) improving comparison of subsets of data, and (ii) identifying anomalies (including sensitivities) in email communication

Reasonableness in E-Discovery

Issues of reasonableness arise regularly throughout American law. Reasonableness is a concept central to tort law, which imposes a reasonable person standard in ascertaining duty. Criminal guilt turns on a reasonable doubt standard. And in civil discovery, the concept of reasonableness features prominently: discovery\u27s scope reaches information that is reasonably calculated to lead to the discovery of admissible evidence, and discovery cannot be unreasonably cumulative or duplicative. Reasonableness standards require judges to undertake an objective, rather than subjective, evaluation. E-discovery specifically has two significant overarching reasonableness components: reasonable accessibility for production and reasonable care in preservation and disclosure. The interpretation of these two components plays a central and determinative role in the effectiveness and burdensomeness in discovering electronically stored information. This Symposium Article addresses the first of these two components - reasonable accessibility - analyzing the guidance available on this issue from the case law and commentators and concluding that current approaches to reasonable accessibility often fail to employ the required objective reasonableness standard. Current approaches tend to err in two prominent ways: (1) by relying inappropriately on informational classifications, and (2) by merging distinct standards into a single standard. Of particular significance, Federal Rule 26 creates a twofold reasonableness interpretation - both with respect to what constitutes reasonable accessibility and also with respect to what constitutes undue burden or expense. However, rather than undertaking an objective, fact-specific inquiry of reasonable accessibility, some courts are relying on categories for presumptive accessibility or inaccessibility. In addition, many courts appear to be evaluating undue burden or expense as one conflated standard that considers only cost

Machine learning in predictive analytics on judicial decision-making

Legal professionals globally are under pressure to provide ‘more for less' – not an easy challenge in the era of big data, increasingly complex regulatory and legislative frameworks and volatile financial markets. Although largely limited to information retrieval and extraction, Machine Learning applications targeted at the legal domain have to some extent become mainstream. The startup market is rife with legal technology providers with many major law firms encouraging research and development through formal legal technology incubator programs. Experienced legal professionals are expected to become technologically astute as part of their response to the ‘more for less' challenge, while legal professionals on track to enter the legal services industry are encouraged to broaden their skill sets beyond a traditional law degree. Predictive analytics applied to judicial decision-making raise interesting discussions around potential benefits to the general public, over-burdened judicial systems and legal professionals respectively. It is also associated with limitations and challenges around manual input required (in the absence of automatic extraction and prediction) and domain-specific application. While there is no ‘one size fits all' solution when considering predictive analytics across legal domains or different countries' legal systems, this dissertation aims to provide an overview of Machine Learning techniques which could be applied in further research, to start unlocking the benefits associated with predictive analytics on a greater (and hopefully local) scale