Machine learning is an area of artificial intelligence that enables computers to self-learn, without explicit programming. In e-discovery, machine-learning technologies such as technology assisted review (TAR) are helping legal teams dramatically speed document review and thereby reduce its cost. TAR learns which documents are most likely relevant and feeds those first to reviewers, typically eliminating the need to review from 50 to 90 percent of a collection.
Lawyers are getting it, as evidenced by their expanding use of TAR. At Catalyst, 50 percent of matters now routinely use TAR—and none have been challenged in court.
Considering that, we thought it would be useful to highlight our most popular TAR-related posts on this blog. These are the posts that received the most traffic during the first five months of this year, regardless of the year the posts were originally published.
As you can see, there is great interest among our readers in case law, which is not surprising. But there’s also interest in when and how to best use TAR.
Here, then, are our top 10 blogs for the first half of 2017:
- Continuous Active Learning for Technology Assisted Review (How it Works and Why it Matters for E-Discovery)
- TAR in the Courts: A Compendium of Case Law about Technology Assisted Review
- Citing TAR Research, Court OKs Production Using Random Sampling
- New Book from Catalyst Answers Your Questions about TAR
- Pioneering Cormack/Grossman Study Validates Continuous Learning, Judgmental Seeds and Review Team Training for Technology Assisted Review
- Ask Catalyst: What Are The Thresholds for Using Technology Assisted Review?
- Video: Understanding Yield Curves in Technology Assisted Review
- Ask Catalyst (Video Edition): How Does TAR Work and Why Does It Matter?
To learn more, we invite you to register now for our June 22 webinar with ACEDS, A User’s Guide to Machine Learning in E-Discovery.