The most effective TAR is electronic document review enhanced by active machine learning, a type of specialized Artificial Intelligence. Our method of AI-enhanced document review is called Hybrid Multimodal IST Predictive Coding 4.0. By the end of the course you will know exactly what this means. You may even grok the above graphic.
Here is the introductory video by Ralph Losey welcoming you to the TAR Course. Ralph makes multiple video appearances throughout the course. More videos will be added from time to time to keep the materials current. Students are both invited to leave comments and questions.
The TAR Course has Sixteen Classes:
- First Class: Introduction to the Course
- Second Class: TREC Total Recall Track, 2015 and 2016
- Third Class: Introduction to the Nine Insights from TREC Research Concerning the Use of Predictive Coding in Legal Document Review
- Fourth Class: 1st of the Nine Insights – Active Machine Learning
- Fifth Class: 2nd Insight – Balanced Hybrid and Intelligently Spaced Training (IST)
- Sixth Class: 3rd and 4th Insights – Concept and Similarity Searches
- Seventh Class: 5th and 6th Insights – Keyword and Linear Review
- Eighth Class: 7th, 8th and 9th Insights – SME, Method, Software; the Three Pillars of Quality Control
- Ninth Class: Introduction to the Eight-Step Work Flow
- Tenth Class: Step One – ESI Communications
- Eleventh Class: Step Two – Multimodal ECA
- Twelfth Class: Step Three – Random Prevalence
- Thirteenth Class: Steps Four, Five and Six – Iterative Machine Training
- Fourteenth Class: Step Seven – ZEN Quality Assurance Tests (Zero Error Numerics)
- Fifteenth Class: Step Eight – Phased Production
- Sixteenth Class: Conclusion
With a lot of hard work you can complete this online training program in a long weekend. After that, this course can serve as a solid reference to consult during your complex document review projects.
We call our latest version of AI enhanced document review taught here “Predictive Coding 4.0.” We call it version 4.0 because it substantially improves upon and replaces the methods and insights we announced in our October 2015 publication – Predictive Coding 3.0. There we explained the history of predictive coding software and methods in legal review, including versions 1.0 and 2.0. Unfortunately, most vendors are still stuck in these earlier methods. If you have tried predictive coding and did not like it, then the probable reason is that you used the vendors recommended, but wrong method. Either that, or the software was to blame, but it is probably the method. Many lawyers report that they attain better results when they follow their own methods, not the vendors default methods.
Most vendors are still promoting use of random based control sets based on a misunderstanding of statistics and search. The use of control sets is simply wrong and a waste of time. We never saw any of these same vendors at TREC and for good reason. They are mostly clueless and are not keeping up with the latest developments in search science. They are a business. We are not. The e-Discovery Team is a group of lawyers, lead by Ralph Losey, a practicing attorney. We are lawyers sharing what we know with other lawyers (and vendors).
We offer this information for free on this blog to encourage as many people as possible in this industry to get on the AI bandwagon. Predictive coding is based on active machine learning, which is a classic, powerful type of Artificial Intelligence (AI). Our Predictive Coding 4.0 method is designed to harness this power to help attorneys find key evidence in ESI quickly and effectively.
In our October 2015 publication – Predictive Coding 3.0 – we introduced version 3.0. We began by explaining the debunked science behind the old vendor methods, version 1.0 and 2.0. We also described our then new version 3.0.
Since that time we have developed more enhancements to our methods thus necessitating a new version 4.0. Still this new version builds upon the last and so we strongly recommend that you read this 3.0 article as background before you begin the course.
Students are invited to leave a public comment below. Insights that might help other students are especially welcome. Let’s collaborate!
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