Last year, spontaneous conversations at ILTA and Relativity Fest brought to light a shared challenge among our clients: managing the progress of their document review and setting the right expectations for their clients. Often, review timelines are tight and every second counts—and the overall success of a case is dependent on meeting aggressive deadlines under budget. However, gathering real-time insight to make forecasts is a manual and arduous process.
How to Deal with Non-Responsive Documents that Contain Responsive Language
After posting a couple months back about our Reviewer Protocol document—which outlines best practices for identifying good example documents while using computer-assisted review—we received a number of requests for it. Though we realize it’s not the Magna Carta or Detective Comics #27, we hope it can be a helpful reference for anyone conducting a Relativity Assisted Review project. We thought it could be useful to dive into some of its content more deeply, and share some detail on how collaborating with our users has helped us continue to improve the protocol.
The late Dr. Alan Turing would have been 100 years old last month, and there was no shortage of memorials to celebrate his life. Dr. Turing’s work made big strides in the world of computer science: he is widely credited with developing the concept of the algorithm, pioneering artificial intelligence, and helping crack the code on the German Enigma machine during WWII.
In 1950, Dr. Turing authored Computing Machinery and Intelligence, a seminal paper on artificial intelligence. In this paper, he pondered the question, “Can machines think?” As part of a fictional test, Dr. Turing describes a game that involves a human interviewer and a hidden interviewee. After a series of questions and without the interviewer’s knowledge, a machine will begin to answer the questions instead of the human interviewee. The trick in this experiment is to see if the computer can answer the questions accurately enough to make the interviewer believe they’re still interviewing another human. In short, when a computer is indistinguishable from a human, we might conclude that a computer can “think” and “learn.”
We’d like to talk briefly about a topic that naturally comes to mind when we think about Relativity and e-discovery—stereo speakers.
It is commonly held, with good reason, that a stereo system is only as good as its speakers. All the power and beauty of the other components mean nothing if the speakers can’t pump some bass. We think the same concept applies to document review in general, but especially to computer-assisted review projects. Restated (and a bit more on topic), your assisted review project is only as good as your reviewers. Your index could be tight as a drum, your de-duping strategy above reproach, your custom-designed workflow the toast of Broadway—yet it simply won’t matter without strong reviewers.