From Bubbles to Lists: Designing Clustering for Due Diligence

Abstract

In due diligence, lawyers are tasked with reviewing a large set of legal documents to identify documents and portions thereof that may be problematic for a merger or acquisition. In an effort to aid users to review more efficiently, we sought to determine how document-level clustering may help users of a due diligence system during their workflow. Following an iterative design methodology, we conducted several user studies with different versions of a document-level clustering feature consisting of three distinct phases and 27 users. We found that the interface should adapt to a user's understanding of what "similar documents" means so that trust can be established in the feature. Furthermore, the ability to negotiate with the underlying algorithm is facilitated by the establishment of trust. Finally, while the usage of this feature may be influenced by a user's role, it remains primarily a project management tool.