Global EDD Group utilizes Rational Intelligence Intelligent Coding Technology from Rational Retention. Rational Intelligence ("RI") enables customers to model characteristics unique to a small sample of documents and to automatically apply that model to code any number of documents. Since the quality of the output depends heavily on the input, subject-matter experts are needed to create a training set of documents for each issue by reviewing documents in a straightforward and natural manner. Typical training sets comprise of only a fraction of the total document population, allowing clients to leverage the knowledge of the best reviewers across the entire dataset. RI's technology experts work directly with clients to design and advise on creating representative and compact training populations, specifically tailored to the document population and the substantive needs of the matter. RI is not limited to coding for responsiveness and privilege – subject-matter experts can train the system for any and every relevant issue. Once a model is run against the entire corpus of available documents, the population is winnowed down to a manageable size for more senior lawyers to review. The result is a more accurate, more consistent, faster document review, conducted at a fraction of the expense of manual review.
Rational Intelligence Intelligent Coding Workflow
RI's Accuracy and Defensibility Versus the Competition
The Rational Intelligence coding toolset is based on the RR patented Markov Boundary, Causal Graph, Support Vector Machine, and other cutting-edge high-dimensional data classification methods. The RR team has established these methods over decades of research and through rigorous testing across thousands of datasets and classification iterations. RR has also addressed the technology and approach with extensive academic peer review through 120 publications, including nine patents, four books, software systems, and academic papers. In text classification, one size does not fit all. Unlike our competitors, RI is able to use different classification methods based upon the unique characteristics of the data at hand. RR recently completed the most comprehensive comparison of classification methods conducted to date: 30 of the most widely applied classification engines were tested against 20 feature extractors across 240 unique data sets. RR created and tested over 100,000 unique state-of-the-art protocols to learn how the technologies perform on various data sets through empirical evidence.
The results led to some important conclusions:
- There were many classification techniques and algorithms common in the marketplace that consistently underperformed;
- All approaches, even underperforming ones, performed well on at least a limited number of datasets, which allows almost any provider to point to a successful result;
- Certain approaches were consistently extremely high-performing, but not universally nor absolutely so;
- Feature compression techniques leading to explainable and transparent models and faster execution of models can be applied while maintaining the performance of the classifiers; and
- Due to the variability in accuracy of the various approaches, having the skills to efficiently deploy multiple approaches and accurately measure the results is paramount to a successful outcome.
- While one method may be best for a particular issue or dataset, it is necessary to be able to adjust classification techniques on a case-by-case basis. Thus, it is critical to have a suite of leading technologies available and the expertise necessary to properly deploy and evaluate the right approach for each case. This strategy is at the core of the Rational Intelligence offering.
To further enhance the defensibility of the classification technology, RI employs rigorous quality control and validation processes to ensure that models' confidence levels are acceptable and accurate, without over-fitting the model to the training set. In addition, the Rational Retention solution transparently displays the unique characteristics of the documents that lead to coding decisions, giving law firms, their clients, and the courts confidence in the quality of the product.
The Team Behind Rational Intelligence
To ensure that our document classification technology was built on the most cutting-edge methodology, Rational Retention ("RR") partnered with a team of leading bioinformatics researchers from the New York University Center for Health Informatics and Bioinformatics, led by Dr. Constantin Aliferis. Working with Rational Retention's leadership team, including Chief Architect, Dr. Konstantin Mertsalov, the Aliferis team used its experience in biomedical applications of classification technology to develop Rational Intelligence. The NYU Health Informatics and Bioinformatics Center, under the leadership of Dr. Aliferis, has made major breakthroughs over the span of the last decade in the development of software, algorithms, and theory in the interpretation of super-high-dimensional data, toward unraveling mechanisms of disease for robust predictive modeling of complex biological systems. Several of the algorithms, protocols, and software developed by the NYU group enjoy thousands of research- and industrial-registered users, including major universities, pharmaceutical companies, and IT companies. Much of their research and innovation involves the classification of unstructured text. They have created and patented Causal Graph and Markov Blanket discovery algorithms to increase accuracy, decrease runtime, reduce the size of training sets, and increase defensibility.