This is part Nine of the continuing series on two-filter document culling. This is very important to successful, economical document review. Please read parts one, two, three, four, five, six, seven and eight before this one.
Second Filter – Predictive Culling and Coding
The second filter begins where the first leaves off. The ESI has already been purged of unwanted custodians, date ranges, spam, and other obvious irrelevant files and file types. Think of the First Filter as a rough, coarse filter, and the Second Filter as fine-grained. The Second Filter requires a much deeper dive into file contents to cull out irrelevance. The most effective way to do that is to use predictive coding, by which I mean active machine learning, supplemented somewhat by using a variety of methods to find good training documents. That is what I call a multimodal approach that places primary reliance on the Artificial Intelligence at the top of the search pyramid. If you do not have active machine learning type of predictive coding with ranking abilities, you can still do fine-grained Second Level filtering, but it will be harder, and probably less effective and more expensive.
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All kinds of Second Filter search methods should be used to find highly relevant and relevant documents for AI training. Stay away from any process that uses just one search method, even if the one method is predictive ranking. Stay far away if the one method is rolling dice. Reliance on random chance alone has been proven to be an inefficient and ineffective way to select training documents. Latest Grossman and Cormack Study Proves Folly of Using Random Search For Machine Training – – Part One and Part Two and Three and Four. No one should be surprised by that.
The first round of training begins with the documents reviewed and coded relevant incidental to the First Filter coding. You could also defer the first round until you have done more active searches for relevant and highly relevant from the pool remaining after First Filter culling. In that case you also include irrelevant in the first training round, which is also important. Note that even though the first round of training is the only round of training that has a special name – seed set – there is nothing all that important or special about it. All rounds of training are important.
There is so much misunderstanding about that, and seed sets, that I no longer like to even use the term. The only thing special in my mind about the first round of training is that it is sometimes a very large training set. That happens when the First Filter turns up a large amount of relevant files, or they are otherwise known and coded before the Second Filter training begins. The sheer volume of training documents in many first rounds thus makes them special, not the fact that the training came first.
No good predictive coding software is going to give special significance to a training document just because it came first in time. (It might if it uses a control set, but that is a different story, explained in my article Predictive Coding 3.0). The software I use has no trouble at all disregarding any early training if it later finds that it is inconsistent with the total training input. It is, admittedly, somewhat aggravating to have a machine tell you that your earlier coding was wrong. But I would rather have an emotionless machine tell me that, than another gloating attorney (or judge), especially when the computer is correct, which is often (not always) the case.
That is, after all, the whole point of using good software with artificial intelligence. You do that to enhance your own abilities. There is no way I could attain the level of recall I have been able to manage lately in large document review projects by reliance on my own, limited intelligence alone. That is another one of my search and review secrets. Get help from a higher intelligence, even if you have to create it yourself by following proper training protocols.