Empirical Study of Deep Learning for Text Classification in Legal Document Review

Predictive coding has been widely used in legal matters to find relevant or privileged documents in large sets of electronically stored information. It saves the time and cost significantly. Logistic Regression (LR) and Support Vector Machines (SVM) are two popular machine learning algorithms used in predictive coding. Recently, deep learning received a lot of attentions in many industries. This paper reports our preliminary studies in using deep learning in legal document review. Specifically, we conducted experiments to compare deep learning results with results obtained using a SVM algorithm on the four datasets of real legal matters. Our results showed that CNN performed better with larger volume of training dataset and should be a fit method in the text classification in legal industry.Comment: 2018 IEEE International Conference on Big Data (Big Data

An Empirical Study of the Application of Machine Learning and Keyword Terms Methodologies to Privilege-Document Review Projects in Legal Matters

Protecting privileged communications and data from disclosure is paramount for legal teams. Unrestricted legal advice, such as attorney-client communications or litigation strategy. are vital to the legal process and are exempt from disclosure in litigations or regulatory events. To protect this information from being disclosed, companies and outside counsel must review vast amounts of documents to determine those that contain privileged material. This process is extremely costly and time consuming. As data volumes increase, legal counsel employ methods to reduce the number of documents requiring review while balancing the need to ensure the protection of privileged information. Keyword searching is relied upon as a method to target privileged information and reduce document review populations. Keyword searches are effective at casting a wide net but return over inclusive results -- most of which do not contain privileged information -- and without detailed knowledge of the data, keyword lists cannot be crafted to find all privilege material. Overly-inclusive keyword searching can also be problematic, because even while it drives up costs, it also can cast `too far of a net' and thus produce unreliable results.To overcome these weaknesses of keyword searching, legal teams are using a new method to target privileged information called predictive modeling. Predictive modeling can successfully identify privileged material but little research has been published to confirm its effectiveness when compared to keyword searching. This paper summarizes a study of the effectiveness of keyword searching and predictive modeling when applied to real-world data. With this study, this group of collaborators wanted to examine and understand the benefits and weaknesses of both approaches to legal teams with identifying privilege material in document populations.Comment: 2018 IEEE International Conference on Big Data (Big Data

Explainable Text Classification in Legal Document Review A Case Study of Explainable Predictive Coding

In today's legal environment, lawsuits and regulatory investigations require companies to embark upon increasingly intensive data-focused engagements to identify, collect and analyze large quantities of data. When documents are staged for review the process can require companies to dedicate an extraordinary level of resources, both with respect to human resources, but also with respect to the use of technology-based techniques to intelligently sift through data. For several years, attorneys have been using a variety of tools to conduct this exercise, and most recently, they are accepting the use of machine learning techniques like text classification to efficiently cull massive volumes of data to identify responsive documents for use in these matters. 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 typical legal `document review' scenarios, a document can be identified as responsive, as long as one or more of the text snippets in a document are deemed responsive. In these scenarios, if predictive coding 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, predictive coding can drastically enhance the overall quality and speed of the document review process by reducing the time it takes to review documents. The authors of this paper propose the concept of explainable predictive coding and simple explainable predictive coding methods to locate responsive snippets within responsive documents. We also report our preliminary experimental results using the data from an actual legal matter that entailed this type of document review.Comment: 2018 IEEE International Conference on Big Dat

The Economic Effects of Unions in Latin America: Teachers' Unions and Education in Argentina

This paper considers the effects of trade unions on the education sector in Argentina. We have provided a substantial amount of new information and we have found useful preliminary results on some of the channels of union influence on the performance of this crucial sector. We find that those provinces where teacher unionism is fragmented, where union density is higher and where political relations with the governor are more conflictual, have more strikes (fewer class days). Based on estimates of education production functions both in this paper and elsewhere, we expect this to translate into lower student performance. We then find a number of weak conclusions related to the impact that unions have on several variables that affect students’ performance (i. e. , teachers’ tenure, job satisfaction, class size, education budget and teachers’ salaries). Reviewing these results, we conclude that the impact of unions on students’ performance depends on the channel and kind of political market where unions operate, but not on the existence of unions per se.

The sustainable delivery of sexual violence prevention education in schools

Sexual violence is a crime that cannot be ignored: it causes our communities significant consequences including heavy economic costs, and evidence of its effects can be seen in our criminal justice system, public health system, Accident Compensation Corporation (ACC), and education system, particularly in our schools. Many agencies throughout New Zealand work to end sexual violence. Auckland-based Rape Prevention Education: Whakatu Mauri (RPE) is one such agency, and is committed to preventing sexual violence by providing a range of programmes and initiatives, information, education, and advocacy to a broad range of audiences. Up until early 2014 RPE employed one or two full-time positions dedicated to co-ordinating and training a large pool (up to 15) of educators on casual contracts to deliver their main school-based programmes, BodySafe – approximately 450 modules per year, delivered to some 20 high schools. Each year several of the contract educators, many of whom were tertiary students, found secure full time employment elsewhere. To retain sufficient contract educators to deliver its BodySafe contract meant that RPE had to recruit, induct and train new educators two to three times every year. This model was expensive, resource intense, and ultimately untenable. The Executive Director and core staff at RPE wanted to develop a more efficient and stable model of delivery that fitted its scarce resources. To enable RPE to know what the most efficient model was nationally and internationally, with Ministry of Justice funding, RPE commissioned Massey University to undertake this report reviewing national and international research on sexual violence prevention education (SVPE)

The sustainable delivery of sexual violence prevention education in schools

Sexual violence is a crime that cannot be ignored: it causes our communities significant consequences including heavy economic costs, and evidence of its effects can be seen in our criminal justice system, public health system, Accident Compensation Corporation (ACC), and education system, particularly in our schools. Many agencies throughout New Zealand work to end sexual violence. Auckland-based Rape Prevention Education: Whakatu Mauri (RPE) is one such agency, and is committed to preventing sexual violence by providing a range of programmes and initiatives, information, education, and advocacy to a broad range of audiences. Up until early 2014 RPE employed one or two full-time positions dedicated to co-ordinating and training a large pool (up to 15) of educators on casual contracts to deliver their main school-based programmes, BodySafe – approximately 450 modules per year, delivered to some 20 high schools. Each year several of the contract educators, many of whom were tertiary students, found secure full time employment elsewhere. To retain sufficient contract educators to deliver its BodySafe contract meant that RPE had to recruit, induct and train new educators two to three times every year. This model was expensive, resource intense, and ultimately untenable. The Executive Director and core staff at RPE wanted to develop a more efficient and stable model of delivery that fitted its scarce resources. To enable RPE to know what the most efficient model was nationally and internationally, with Ministry of Justice funding, RPE commissioned Massey University to undertake this report reviewing national and international research on sexual violence prevention education (SVPE). [Background from Executive Summary.]Rape Prevention Education: Whakatu Maur

Implementation conditions for diet and physical activity interventions and policies : an umbrella review

BACKGROUND: This umbrella review aimed at identifying evidence-based conditions important for successful implementation of interventions and policies promoting a healthy diet, physical activity (PA), and a reduction in sedentary behaviors (SB). In particular, we examined if the implementation conditions identified were intervention-specific or policy-specific. This study was undertaken as part of the DEterminants of DIet and Physical Activity (DEDIPAC) Knowledge Hub, a joint action as part of the European Joint Programming Initiative a Healthy Diet for a Healthy Life. METHODS: A systematic review of reviews and stakeholder documents was conducted. Data from nine scientific literature databases were analyzed (95 documents met the inclusion criteria). Additionally, published documentation of eight major stakeholders (e.g., World Health Organization) were systematically searched (17 documents met the inclusion criteria). The RE-AIM framework was used to categorize elicited conditions. Across the implementation conditions 25 % were identified in at least four documents and were subsequently classified as having obtained sufficient support. RESULTS: We identified 312 potential conditions relevant for successful implementation; 83 of these received sufficient support. Using the RE-AIM framework eight implementation conditions that obtained support referred to the reach in the target population; five addressed efficacy of implementation processes; 24 concerned adoption by the target staff, setting, or institutions; 43 referred to consistency, costs, and adaptations made in the implementation process; three addressed maintenance of effects over time. The vast majority of implementation conditions (87.9 %; 73 of 83) were supported by documents referring to both interventions and policies. There were seven policy-specific implementation conditions, which focused on increasing complexities of coexisting policies/legal instruments and their consequences for implementation, as well as politicians' collaboration in implementation. CONCLUSIONS: The use of the proposed list of 83 conditions for successful implementation may enhance the implementation of interventions and policies which pursue identification of the most successful actions aimed at improving diet, PA and reducing SB