Archive for the ‘Uncategorized’ Category

Sampling with zero intent

Thursday, March 15th, 2018

A zero intent sample is a sample which will only satisfy our validation goal if no positive examples are found in it. If we have a population (in e-discovery, typically a document set) where one in R instances are positive (one in R documents relevant), and we only want a one in Q probability of sampling a positive instance, then our sample size can be no more than R / Q.

Back from the other side

Wednesday, March 14th, 2018

Well, after a couple of years at FTI, and some, ahem, self-funded gardening leave, I'm back to consulting---and to blogging! More from me soon.

Off to FTI: see you on the other side

Sunday, January 18th, 2015

Tomorrow I'm starting a new, full-time position as data scientist at FTI's lab here in Melbourne. I'm excited to have the opportunity to contribute to the e-discovery community from another angle, as a builder-of-product. Unfortunately, this means the end of this blog, at least in its current form and at least for now. Thanks to all my readers, commenters, and draft-post-reviewers. It's been an entertaining experience!

Confidence intervals on recall and eRecall

Sunday, January 4th, 2015

There is an ongoing discussion about methods of estimating the recall of a production, as well as estimating a confidence interval on that recall. One approach is to use the control set sample, drawn at the start of production to estimate collection richness and guide the predictive coding process, to also estimate the final confidence interval. This requires some care, however, to avoid contaminating the control with the training set. Using the control set for the final estimate is also open to the objection that the control set coding decisions, having been made before the subject-matter expert (SME) was familiar with the collection and the case, may be unreliable.

Why training and review (partly) break control sets

Monday, October 20th, 2014

A technology-assisted review (TAR) process frequently begins with the creation of a control set---a set of documents randomly sampled from the collection, and coded by a human expert for relevance. The control set can then be used to estimate the richness (proportion relevant) of the collection, and also to gauge the effectiveness of a predictive coding (PC) system as training is undertaken. We might also want to use the control set to estimate the completeness of the TAR process as a whole. However, we may run into problems if we attempt to do so.

The reason the control set can be used to estimate the effectiveness of the PC system on the collection is that it is a random sample of that collection. As training proceeds, however, the relevance of some of the documents in the collection will become known through human assessment---even more so if review begins before training is complete (as is often the case). Direct measures of process effectiveness on the control set will fail to take account of the relevant and irrelevant documents already found through human assessment.

Total assessment cost with different cost models

Thursday, October 16th, 2014

In my previous post, I found that relevance and uncertainty selection needed similar numbers of document relevance assessments to achieve a given level of recall. I summarized this by saying the two methods had similar cost. The number of documents assessed, however, is only a very approximate measure of the cost of a review process, and richer cost models might lead to a different conclusion.

One distinction that is sometimes made is between the cost of training a document, and the cost of reviewing it. It is often assumed that training is performed by a subject-matter expert, whereas review is done by more junior reviewers. The subject-matter expert costs more than the junior reviewers---let's say, five times as much. Therefore, assessing a document for relevance during training will cost more than doing so during review.

Total review cost of training selection methods

Saturday, September 27th, 2014

My previous post described in some detail the conditions of finite population annotation that apply to e-discovery. To summarize, what we care about (or at least should care about) is not maximizing classifier accuracy in itself, but minimizing the total cost of achieving a target level of recall. The predominant cost in the review stage is that of having human experts train the classifier, and of having human reviewers review the documents that the classifier predicts as responsive. Each relevant document found in training is one fewer that must be looked at in review. Therefore, training example selection methods such as relevance selection that prioritize relevant documents are likely to have a lower total cost than the abstract measure of classifier effectiveness might suggest.

Finite population protocols and selection training methods

Monday, September 15th, 2014

In a previous post, I compared three methods of selecting training examples for predictive coding—random, uncertainty and relevance. The methods were compared on their efficiency in improving the accuracy of a text classifier; that is, the number of training documents required to achieve a certain level of accuracy (or, conversely, the level of accuracy achieved for a given number of training documents). The study found that uncertainty selection was consistently the most efficient, though there was no great difference betweein it and relevance selection on very low richness topics. Random sampling, in contrast, performs very poorly on low richness topics.

In e-discovery, however, classifier accuracy is not an end in itself (though many widely-used protocols treat is as such). What we care about, rather, is the total amount of effort required to achieve an acceptable level of recall; that is, to find some proportion of the relevant documents in the collection. (We also care about determining to our satisfaction, and demonstrating to others, that that level of recall has been achieved—but that is beyond the scope of the current post.) A more accurate classifier means a higher precision in the candidate production for a given level of recall (or, equivalently, a lesser cutoff depth in the predictive ranking), which in turn saves cost in post-predictive first-pass review. But training the classifier itself takes effort, and after some point, the incremental saving in review effort may be outweighted by the incremental cost in training.

Research topics in e-discovery

Friday, August 8th, 2014

Dr. Dave Lewis is visiting us in Melbourne on a short sabbatical, and yesterday he gave an interesting talk at RMIT University on research topics in e-discovery. We also had Dr. Paul Hunter, Principal Research Scientist at FTI Consulting, in the audience, as well as research academics from RMIT and the University of Melbourne, including Professor Mark Sanderson and Professor Tim Baldwin. The discussion amongst attendees was almost as interesting as the talk itself, and a number of suggestions for fruitful research were raised, many with fairly direct relevance to application development. I thought I'd capture some of these topics here.

Random vs active selection of training examples in e-discovery

Thursday, July 17th, 2014

The problem with agreeing to teach is that you have less time for blogging, and the problem with a hiatus in blogging is that the topic you were in the middle of discussing gets overtaken by questions of more immediate interest. I hope to return to the question of simulating assessor error in a later post, but first I want to talk about an issue that is attracting attention at the moment: how to select documents for training a predictive coding system.