FOMO: Topics versus documents in legal eDiscovery

TL;DR

This paper models the process of legal eDiscovery as identifying responsive information rather than documents, using probabilistic models to estimate the likelihood of missing relevant topics and analyzing real data sets to validate the approach.

Contribution

It introduces a simple probabilistic model for estimating the chance of omitting relevant information in eDiscovery, focusing on topics within documents rather than individual documents.

Findings

At least one example of each topic can be found with relatively few documents.

Non-random search order does not affect topic distribution.

The model helps estimate confidence in capturing all relevant information.

Abstract

In the United States, the parties to a lawsuit are required to search through their electronically stored information to find documents that are relevant to the specific case and produce them to their opposing party. Negotiations over the scope of these searches often reflect a fear that something will be missed (Fear of Missing Out: FOMO). A Recall level of 80%, for example, means that 20% of the relevant documents will be left unproduced. This paper makes the argument that eDiscovery is the process of identifying responsive information, not identifying documents. Documents are the carriers of the information; they are not the direct targets of the process. A given document may contain one or more topics or factoids and a factoid may appear in more than one document. The coupon collector's problem, Heaps law, and other analyses provide ways to model the problem of finding information from among documents. In eDiscovery, however, the parties do not know how many factoids there might be in a collection or their probabilities. This paper describes a simple model that estimates the confidence that a fact will be omitted from the produced set (the identified set), while being contained in the missed set. Two data sets are then analyzed, a small set involving microaggressions and larger set involving classification of web pages. Both show that it is possible to discover at least one example of each available topic within a relatively small number of documents, meaning the further effort will not return additional novel information. The smaller data set is also used to investigate whether the non-random order of searching for responsive documents commonly used in eDiscovery (called continuous active learning) affects the distribution of topics-it does not.