What's inside the Black Box?

A recent study reported that more than half of Fortune 1000 and American Lawyer 200 attorneys noted concern about effectively defending the results of predictive coding.  Predictive Coding to many is a black box. Envize^TM is the latest from iControl ESI, publisher of Recenseo. Envize^TM will change the way you use Predictive Coding.  Envize^TM allows you, the user, to see inside the black box and control the process yourself with a UI unlike any on the market. You won’t need a PhD to guide you. Envize^TM is self guided and you can use Recenseo OR your existing review tool.  

iControl Intellectual property utilized in this tool is not new.  iControl has been using the underlying technology in our own software for several years, only recently giving it a name and Productizing the technology for use by anyone.   Envize^TM utilizes either passive or active learning, allowing you to have visibility into exactly where you are in the process and how well the technology is learning what documents you think are important.  Envize^TM is based on sound and scientifically scrutinized underlying technology that has been accepted by the academic community.  In 2015, the Computer Science department of Indiana and Purdue Universities co-authored and published an academic paper on iControl ESI's methods.  This impressive academic paper - Batch-Mode Active Learning for Technology-Assisted Review*  -  was submitted to, accepted by and presented at the :   IEEE Big Data 2015 Industry & Government Conference - Submission: N216.  The underlying technology is the work of years of research and testing.  Below is an abstract of that academic paper.

"Abstract—In recent years, technology-assisted review (TAR) has become an increasingly important component of the document review process in litigation discovery. This is fueled largely by dramatic growth in data volumes that may be associated with many matters and investigations. Potential review populations frequently exceed several hundred thousands documents, and document counts in the millions are not uncommon. Budgetary and/or time constraints often make a once traditional linear review of these populations impractical, if not impossible—which made “predictive coding” the most discussed TAR approach in recent years. A key challenge in any predictive coding approach is striking the appropriate balance in training the system. The goal is to minimize the time that Subject Matter Experts spend in training the system, while making sure that they perform enough training to achieve acceptable classification performance over the entire review population. Recent research demonstrates that Support Vector Machines (SVM) perform very well in finding a compact, yet effective, training dataset in an iterative fashion using batch-mode active learning. However, this research is limited. Additionally, these efforts have not led to a principled approach for determining the stabilization of the active learning process. In this paper, we propose and compare several batchmode active learning methods which are integrated within SVM learning algorithm. We also propose methods for determining the stabilization of the active learning method. Experimental results on a set of large-scale, real-life legal document collections validate the superiority of our method over the existing methods for this task."

You don’t need a PhD behind the scenes working levers.  Envize^TM allows multiple sampling methods and easy setup. 

Envize^TM provides multiple ways to keep score, including our own Envize^TM Score that tells you exactly where you stand at any given moment.

So, What Makes This Different?

• Start Training Faster (with or without control set)
• Finish Training Faster (with or without control set)
• Better handling of rolling population changes
• Envize Automated Project Analysis and Recommendations
• Better performance measure
• Better Review Quality Estimates

Software and Services Since 1999

To Learn More....

Contact:  mwalker@icontrolesi.com