Total Review Cost of Training Selection Methods

By William Webber

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 […]