This is Fourteenth and Final blog in a series on two-filter document culling. (Yes, we went for and obtained a world record on longest law blog series!) Document culling is very important to successful, economical document review. Please read parts one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve and thirteen before this one.
There is much more to efficient, effective review than just using software with predictive coding features. The methodology of how you do the review is critical. The two filter method described here has been used for years to cull away irrelevant documents before manual review, but it has typically just been used with keywords. I have shown in this lengthy series of blogs how this method can be employed in a multimodal manner that includes predictive coding in the Second Filter.
Keywords can be an effective method to both cull out presumptively irrelevant files, and cull in presumptively relevant, but keywords are only one method, among many. In most projects it is not even the most effective method. AI-enhanced review with predictive coding is usually a much more powerful method to cull out the irrelevant and cull in the relevant and highly relevant.
If you are using a one-filter method, where you just do a rough cut and filter out by keywords, date, and custodians, and then manually review the rest, you are reviewing too much. It is especially ineffective when you collect based on keywords. As shown in Biomet, that can doom you to low recall, no matter how good your later predictive coding may be.
If you are using a two-filter method, but are not using predictive coding in the second filter, you are still reviewing too much. The two-filter method is far more effective when you use relevance probability ranking to cull out documents from final manual review.