Text IQ Blog

Transforming Responsive and Privilege Reviews with Artificial Intelligence

“AI” is a lofty term that doesn’t have a universal definition. And in fact, AI is not even new. The field of research in artificial intelligence was established at Dartmouth College in 1956. The term “machine learning” was coined three years later by a man named Arthur Samuel, who taught a computer to play checkers.

Examining the Class Action Discovery Burden

Rule 23 of the Federal Rules of Civil Procedure and its state analogues, which govern class action lawsuits in the United States, may be the most important laws in modern litigation.  In an era when most civil suits settle, class actions dominate the settlement landscape. Given the trend towards higher settlement values, recent Supreme Court opinions that have been interpreted as anti-class-plaintiff are turning out to challenge class action defendants. In this article, we examine these recent rulings, trends, and challenges in class action litigation settlements.

How AI Can Overcome the 5 Choke Points in Privilege Review

At Text IQ, we developed our solution because we found many stages in the privilege review process where current methods were inefficient and inherently risky. These stages represent choke points that our technology—AI for Sensitive Information—can overcome.

This AI-Powered Workflow is Transforming Privilege Review

Text IQ has applied artificial intelligence to privilege review, a process that is historically risky, inefficient, and costly. The diagram below demonstrates the literal transmutation of privilege review with Text IQ.

Operationalizing Machine Learning for GDPR

An unstable physical system will tend toward equilibrium. With its General Data Protection Regulation, Europe is subjecting United States companies to a regulation that threatens some of their core business models and data infrastructures. The tension between these two regions will eventually settle, and when that happens, the ways we approach data and think about privacy will be fundamentally altered.

When Unsupervised Machine Learning Becomes Human Teaching

My favorite machine learning study is not from a machine learning journal, but from a study about the Ancient Chinese. This says a lot about the versatility of AI.

Meet a Text IQer: Senior R&D Engineer Daven Corbett

We sat down with Daven – who studied Computer Science at Stony Brook University and comes to us via enterprise software companies like Salesforce – to discuss high stakes, custom query languages, and cool co-workers.

Sorting Through the Options in Technology-Assisted Second Request Review

Part Two of a Three-Part Series where we examine the evolving challenges of second requests, and the promise of AI to help. Our last post addressed the logistical challenges for antitrust counsel facing a second request review. This post will explore competing demands of growing scrutiny and growing risk.

Facing the Bad Odds of Second Request Review

Part I of a series where we examine what you’re up against, and follow up with the promise of AI to help. 

Amid the high-stakes and often high-stress negotiations that accompany corporate mergers and acquisitions, a government-mandated “Second Request” poses another formidable hurdle for a company and its antitrust counsel to clear.

Why the ESI Explosion Demands New Tools for Document Review

Part 1 of a series about the role of AI in a world that’s leaving TAR behind.