Array Case Studies

Ever since Relativity announced their new GenAI aiR suite of tools based on OpenAI's GPT-4 model, there has been plenty of speculation on how these new tools will work and how we can best incorporate them into our everyday eDiscovery practices. Today we will be looking at some of the emerging use cases for the first of this suite of tools - Relativity's aiR for Review. 

How can aiR for Review be used?

• Boosting efficiencies in review
• Responsiveness review 
• QC human review 
• Train your Active Learning model 
• Replacing issue coding 
   

  Boosting efficiencies in review:

  aiR for Review leverages a 'bottoms up' approach, meaning that it analyses each document as opposed to mass glazing over a group of documents. This means that the tool is ideally suited to supporting reviewers with your document review processes. You can choose how you utilise the tool, but the two most common uses can be found below...
   
  

  Responsiveness review(AI reviews, humans QC)

  
  The first and most common way we see the aiR for Review tool being used is to supplement human reviewers at the first-pass review stage. This is often the most time-consuming and tedious part of the review process and is therefore perfect for machine intervention. aiR for Review can work alongside your reviewers to speed up their review time and reduce the burden on your team.
  
  Based on your review protocol, the tool can be trained on a small number of documents and run across your workspace at the beginning of the review process. The tool will identify likely hot, likely relevant, unlikely hot and unlikely relevant documents that will allow your reviewers to prioritise the documents they look at throughout their review.
  
  The grounding of the tool in quotations also allows the reviewers to see at a glance if the pulled quotation is relevant to the matter. When looking at the document in the viewer, the quotation is highlighted to enable the reviewers to easily locate the most important parts of a document. 
  
  Throughout this process, the reviewers are still responsible for monitoring the AI algorithm and ensuring that the documents it identifies as pertinent are relevant to the case.