ARCHIVED CONTENT
You are viewing ARCHIVED CONTENT released online between 1 April 2010 and 24 August 2018 or content that has been selectively archived and is no longer active. Content in this archive is NOT UPDATED, and links may not function.Extract from New TAR Training by eDiscovery Expert Ralph Losey. Content shared by permission of course author.
This is the e-Discovery Team’s training course on how to do TAR (Technology Assisted Review). What TAR really means 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. The Course is composed of Sixteen Classes:
- First Class: Introduction
- Second Class: TREC Total Recall Track
- Third Class: Introduction to the Nine Insights Concerning the Use of Predictive Coding in Legal Document Review
- Fourth Class: 1st of the Nine Insights – Active Machine Learning
- Fifth Class: Balanced Hybrid and Intelligently Spaced Training
- Sixth Class: Concept and Similarity Searches
- Seventh Class: Keyword and Linear Review
- Eighth Class: GIGO, QC, SME, Method, Software
- 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 – Iterate
- Fourteenth Class: Step Seven – ZEN Quality Assurance Tests
- 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.
First Class: Introduction
The sixteen classes in this course cover seventeen topics:
- Active Machine Learning (aka Predictive Coding)
- Concept & Similarity Searches (aka Passive Learning)
- Keyword Search (tested, Boolean, parametric)
- Focused Linear Search (key dates & people)
- GIGO & QC (Garbage In, Garbage Out) (Quality Control)
- Balanced Hybrid (man-machine balance with IST)
- SME (Subject Matter Expert, typically trial counsel)
- Method (for electronic document review)
- Software (for electronic document review)
- Talk (step 1 – relevance dialogues)
- ECA (step 2 – early case assessment using all methods)
- Random (step 3 – prevalence range estimate, not control sets)
- Select (step 4 – choose documents for training machine)
- AI Rank (step 5 – machine ranks documents according to probabilities)
- Review (step 6 – attorneys review and code documents)
- Zen QC (step 7 – Zero Error Numerics Quality Control procedures)
- Produce (step 8 – production of relevant, non-privileged documents)
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.
- View the complete course overview at TAR Course
- Read the companion course announcement post
- View additional eDiscovery training via ComplexDiscovery