For decades, it was a rite of passage: sequestered in windowless rooms, surrounded by boxes of documents, and scolded by well-heeled partners, associates at top law firms had to “put in their time” doing document review for litigation discovery. Document review for discovery is a laborious and repetitive task akin to searching for a needle in a haystack. It is initiated in the earliest stages of a litigation proceeding and allows for each side of the dispute to gain access to relevant evidence that isn’t considered privileged communication. The trouble comes in sifting through mountains of irrelevant documentation to find responsive documents.
In the pre-digital era of law, discovery meant reviewing physical documents (external correspondence, internal memoranda, sales contracts, and more). In the digital age, this information has moved from filing cabinets to hard drives. With the move toward digital, though, has come a proliferation of data – International Data Corporation predicts that the digital universe will expand to 5,200 gigabytes of data for every human by 2020[i], the equivalent of over 5,000 truckloads of documents! kCura, the maker of Relativity e-discovery software, has positioned itself to take advantage of the opportunities presented by the digitization challenge that faces the legal industry.
Breaking the Tradeoff
The business model of document review firms has always revolved around the customer promises of speed, cost, and accuracy. Tradeoffs and tension bubbled beneath the value proposition: going faster required larger teams and more layers of management and thus increased cost, accuracy meant hiring smarter people who demanded higher wages and thus increased cost (and even then did not guarantee accuracy[ii]), and increasing accuracy often meant adding QC stages and thus slowed the process. kCura’s approach, though, is to break these tradeoffs with the application of e-discovery technologies for a faster, cheaper, and more accurate review.
Delivering the Unthinkable
The earliest use of technology in the review process amounted to little more than keyword-searches that weeded out large percentages of documents that did not contain relevant keywords. This process, while faster and cheaper than the manual review of yore, had major accuracy problems as responsive, non-privileged documents were ignored simply on account of the absence of certain human-picked keywords, with the FTI Journal noting that “such searches have been shown to produce as little as 20 percent of the relevant documents, along with many that are irrelevant.”[iii]
While e-discovery as an industry has taken off in relation to the increase of ESI (electronically-stored information), kCura’s Relativity technology is leading the way. Driven by proprietary “predictive coding” software, Relativity uses TAR (technology-assisted review), or supervised machine learning, to “show” computers what is responsive instead of “telling” them.
Building the Future
While Relativity has driven forward a major shift in how discovery is conducted, it’s technology is quickly becoming commoditized. Given their first-mover advantage, however, and the strong reputation they have established with key clients, there are several opportunities for them to stay one step ahead of the digitization challenge.
- Expand laterally into new markets – the same consumers (large corporates) could benefit from the same technology (technology-assisted review) in areas like commercial contracts and mergers & acquisitions.
- Expand vertically into higher-value add phases of litigation – while the lions’ share of cost occurs in the review phase, the most profitable categories of work lie farther up the food chain. Relativity could get involved in research, brief-preparation, and litigation prediction.
- Develop more advanced machine learning technology to further deliver on the customer promise of speed, cost, and accuracy – while supervised machine learning represents a major achievement over previous technological and manual forms of review, it is not foolproof. Unsupervised machine learning may further eliminate bias in the development of conceptual maps that lay bare relations previously unknown in the data.
While the e-discovery space will only grow to make the $250 billion market more efficient, Relativity is also at risk of significant regulatory intrusion. Legal services in the US are highly regulated, and the introduction of new technologies and business models has historically brought accusations from trade groups of UPL, or the unauthorized practice of law. Current predictive coding practices, while relatively untested in the courts, have fared well enough to-date. In Rio Tinto Plc v. Vale S.A., for example, the court ruled heartily in favor of the use of Relativity to deliver consistency in the production of documents[iv].
Lawyers need not fear an imminent threat – by freeing them from musky basement offices and papercuts, Relativity allows them to “put their time in” on much higher value add activities… until those activities, too, are better completed by computers.