Active Learning works best when you analyze family members when making responsiveness calls. True or False?

The statement that Active Learning works best when you analyze family members when making responsiveness calls is false. Active Learning is a machine learning technique that enhances the process of document review, specifically in the context of e-discovery, by utilizing algorithms to identify which documents are most relevant based on prior training sets.

In this context, analyzing family members—documents that are related or associated within a family (such as emails and attachments)—is a crucial part of the process. However, the effectiveness of Active Learning does not solely depend on the analysis of family members; it is designed to maximize efficiency in document categorization and often relies on larger contexts and patterns rather than just family relationships.

Furthermore, generalizing that the analysis of family members is the most effective practice may overlook other significant factors such as the complexity of the data set, the nature of the projects involved, and the specific criteria established for relevancy. Thus, the applicability of Active Learning methodologies is broader and not strictly dependent on the analysis of family members.