June 18, 2013
Predictive Coding and Technology-Assisted
Review: Hype or Need?
Over the last few years, predictive coding has led the way as a hot topic. Recently the new buzzwords have been technology-assisted review and computer-assisted review.
While these are nice words to throw around, questions arise: How many cases have any of these been applied and what are the metrics of savings and time to the client? How many cases actually require the use of these items?
I can recall that this was the way of future document review, but we find only a few years later that predictive coding has taken a long time to grow. This could be from various factors: hype information about the benefits and risks, though it could be an important part of your e-discovery toolkit but you will need to determine whether predictive coding is right for your particular case and weight the cost savings from a finical point, compare what the cost would be to implement vs. not to implement, your dead line and resources.
In a recent article, “7 E-Discovery Takeaways from CEIC,” three items stood out to me: (1) humans will still play a vital role in document review, but technology will assist in reducing the number of documents; (2) applying text analytics can also reduce the cost and population of document review; (3) make sure you have an agreement with opposing counsel on which technology you will choice. Here are thoughts from the article’s author, Sean Doherty:
1. Technology-assisted review, computer-assisted review, or predictive coding technologies will not replace human review, according to David Cohen, partner at Reed Smith. He said that document review technologies will still need humans to train systems and provide quality control. The undertow to the message: document review is not a career path.
2. Applying text analytics to document review reduces the cost of document review from dollars to cents per document. According to Cohen, human review, on average, can cost $1 to $3 per document. With Equivio, Reed Smith charges clients 3 cents per document after the system is trained, said Cohen. When asked how the firm calculates the cost to train a technology-assisted review system, Cohen said that Reed Smith charges clients by the billable hour.
3. If you can't get an agreement with your opponent on the use of technology-assisted review, said Cohen, don't forge ahead on your own (see Kleen Products v. Packaging Corp.). But there are still three use cases for TAR that don't require an agreement with your opposing party: use it on the production set your opponent delivers to you; use it to provide quality control to manual or human review; or use it to accelerate human review where automated review systems can identify highly relevant and irrelevant documents, which can be grouped or batched to speed review.
My advice to end users is to consult with your internal support team, whether it is the IT department or litigation support. Outline your goals and objectives for each project prior to making your selection on which technology can benefit your goal.
It is easy to become overwhelmed with information or want to use the newest, best, and greatest technology in the market. However, you must consider that the use of higher-end technologies come at a much higher cost. Consider the following while making your choice: (1) remember that sometimes the most cost-effective and efficient solution is also the most straightforward; (2) have a well-defined scope of the data you are going to process and review, because the days of "collect and process everything" are long gone; and (3) plan and map out your strategy for the production and review.
A successful production and review features the following elements:
- Communication and planning
- Meet and confer
- Bring in the experts (internal resources, vendor, review team)
- Outline: scope, goals, deadlines
- Daily progress and quality control checks
Another helpful practice is to reduce data prior to production and review. This procedure includes the following:
- Strategic collection of data
- Strategic filtering of data
- Keeping your ESI in a native format
- Apply text analytics
—Robert Grande, codeMantra, Plymouth Meeting, PA
http://apps.americanbar.org/litigation/committees/technology/news.html#01
