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

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

Proceedings of the 15th Australian Digital Forensics Conference, 5-6 December 2017, Edith Cowan University, Perth, Australia

Conference Foreword This is the sixth year that the Australian Digital Forensics Conference has been held under the banner of the Security Research Institute, which is in part due to the success of the security conference program at ECU. As with previous years, the conference continues to see a quality papers with a number from local and international authors. 8 papers were submitted and following a double blind peer review process, 5 were accepted for final presentation and publication. Conferences such as these are simply not possible without willing volunteers who follow through with the commitment they have initially made, and I would like to take this opportunity to thank the conference committee for their tireless efforts in this regard. These efforts have included but not been limited to the reviewing and editing of the conference papers, and helping with the planning, organisation and execution of the conference. Particular thanks go to those international reviewers who took the time to review papers for the conference, irrespective of the fact that they are unable to attend this year. To our sponsors and supporters a vote of thanks for both the financial and moral support provided to the conference. Finally, to the student volunteers and staff of the ECU Security Research Institute, your efforts as always are appreciated and invaluable. Yours sincerely, Conference ChairProfessor Craig ValliDirector, Security Research Institute Congress Organising Committee Congress Chair: Professor Craig Valli Committee Members: Professor Gary Kessler – Embry Riddle University, Florida, USA Professor Glenn Dardick – Embry Riddle University, Florida, USA Professor Ali Babar – University of Adelaide, Australia Dr Jason Smith – CERT Australia, Australia Associate Professor Mike Johnstone – Edith Cowan University, Australia Professor Joseph A. Cannataci – University of Malta, Malta Professor Nathan Clarke – University of Plymouth, Plymouth UK Professor Steven Furnell – University of Plymouth, Plymouth UK Professor Bill Hutchinson – Edith Cowan University, Perth, Australia Professor Andrew Jones – Khalifa University, Abu Dhabi, UAE Professor Iain Sutherland – Glamorgan University, Wales, UK Professor Matthew Warren – Deakin University, Melbourne Australia Congress Coordinator: Ms Emma Burk

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

Just and Speedy: On Civil Discovery Sanctions for Luddite Lawyers

This article presents a theoretical model by which a judge could impose civil sanctions on an attorney - relying in part on Rule 1 of the Federal Rules of Civil Procedure - for that attorney’s failure to utilize time- and expense-saving technology. Rule 1 now charges all participants in the legal system to ensure the “just, speedy and inexpensive” resolution of disputes. In today’s litigation environment, a lawyer managing a case in discovery needs robust technological competence to meet that charge. However, the legal industry is slow to adopt technology, favoring “tried and true” methods over efficiency. This conflict is evident in data showing clients’ and judges’ frustration with the lack of technological competency among the Bar, especially as it pertains to electronic discovery. Such frustration has led judges to publicly scold luddite attorneys, and has led state bar associations to pass anti-luddite ethical rules. Sanctions for “luddite” attorneys are an extreme, but theoretically possible, amplification of that normative movement. Sanctions leveraging Rule 1 require a close reading of the revised rule, and challenge the notion of Rule 1 as a “guide” to the Rules, but a case can be made for such sanctions based on the Rule’s affirmative charge. The article briefly explores two examples of conduct warranting such sanctions in rare scenarios such as: (1) the failure of an attorney to utilize machine intelligence and concept analytics in the review of information for production and (2) the failure of an attorney to produce documents in accessible electronic format. The article concludes by suggesting that well-publicized sanctions for “luddite” attorneys may break through the traditional barriers that limit innovation in the legal industry

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

The Shifting Sands of Cost Shifting

The cost-shifting analysis employed by the federal courts in ruling on discovery disputes is flawed. There is tremendous variability in how courts interpret the factors guiding the analysis. There is tremendous variability in the information courts rely on in deciding whether to preclude the discovery or shift its costs. The result is waste for the litigants, courts, and society as a whole. This Article argues that there is a better way: mandate cooperation before cost shifting. The courts should condition proportionality and cost-shifting rulings on cooperation. The cooperation should be substantive: require disclosure of objective information about the disputed discovery and, if costs are shared, share control over the process. Cooperation will not come about by exhortation or proclamation; it will come if the cost of discovery, or the discovery itself, hangs in the balance. With that comes the possibility of reducing costs. Asking “is the discovery unduly burdensome” results in a different answer than asking “can it be done more efficiently”? This Article argues that the courts should ask the latter question first and require cooperation in answering it. Federal Rule of Civil Procedure 1, with its mandate to construe the rules to seek the “just, speedy, and inexpensive determination of every action,” demands it

Forensic acquisition of file systems with parallel processing of digital artifacts to generate an early case assessment report

A evolução da maneira como os seres humanos interagem e realizam tarefas rotineiras mudou nas últimas décadas e uma longa lista de atividades agora somente são possíveis com o uso de tecnologias da informação – entre essas pode-se destacar a aquisição de bens e serviços, gestão e operações de negócios e comunicações. Essas transformações são visíveis também em outras atividades menos legítimas, permitindo que crimes sejam cometidos através de meios digitais. Em linhas gerais, investigadores forenses trabalham buscando por indícios de ações criminais realizadas por meio de dispositivos digitais para finalmente, tentar identificar os autores, o nível do dano causado e a história atrás que possibilitou o crime. Na sua essência, essa atividade deve seguir normas estritas para garantir que as provas sejam admitidas em tribunal, mas quanto maior o número de novos artefatos e maior o volume de dispositivos de armazenamento disponíveis, maior o tempo necessário entre a identificação de um dispositivo de um suspeito e o momento em que o investigador começa a navegar no mar de informações alojadas no dispositivo. Esta pesquisa, tem como objetivo antecipar algumas etapas do EDRM através do uso do processamento em paralelo adjacente nas unidades de processamento (CPU) atuais para para traduzir multiplos artefactos forenses do sistema operativo Windows 10 e gerar um relatório com as informações mais cruciais sobre o dispositivo adquirido. Permitindo uma análise antecipada do caso (ECA) ao mesmo tempo em que uma aquisição completa do disco está em curso, desse modo causando um impacto mínimo no tempo geral de aquisição