Artificial Intelligence

Artificial Intelligence Will Change E-Discovery in the Next Three Years

In the late 19th century, the electricity went from being a novelty, little more than the subject of scientific experimentation, to fundamentally altering almost every aspect of American life and industry. Machines could operate faster, cleaner, and more powerfully. The workday was extended, allowing factories to operate two and three times longer every day. Manual tasks were automated.

According to Andrew Ng, Co-Founder of Coursera and Adjunct Professor of Computer Science at Stanford University, artificial intelligence (AI) is the new electricity. “Just as electricity transformed almost everything 100 years ago,” he explains, “today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years.”

Ng is not alone. Consumers’ lives, tastes, and habits have been profoundly altered by artificial intelligence, with companies like Amazon, Google, Netflix, Spotify, and Uber (to name a few) disrupting well-established industries. Legal technology including e-discovery (and software as a service in general) will not be spared. No less an authority than Gartner estimates that 80% of emerging technologies will be built on a foundation of artificial intelligence by 2021.

But what might the future of e-discovery technology look like? Putting on a prognosticator’s cap, three big changes to e-discovery seem all but certain.

  1. Using AI will become “frictionless,” meaning that it will be ever more seamlessly integrated into the e-discovery process.
  2. AI will move out of the review phase, earlier in the EDRM, helping legal teams get to the facts of the matter faster, cheaper, and smarter than ever before.
  3. AI will play an increasing role in orchestrating the e-discovery process, streamlining the process and improving efficiency.

Frictionless AI

While AI has been used in the review phase of e-discovery for approximately a decade, its current integration into document review has become simpler and more elegant. In the past, document review technology required seed sets and users who could define the parameters for relevance. This extra step may have been a worthwhile investment of time in large cases with vast amounts of electronically stored information (ESI), but in smaller matters, the time investment and the cost of the technology may have proved to be a detriment to adoption. After all, in most surveys, including the Blickstein Group’s 10th Annual Law Department Operations Survey, almost 2/3 of law department did not use AI at all.

Today, deep learning algorithms, which simulate the human brain by combining several layers of neural networks, can observe as human attorneys review documents, learning the criteria that make a document relevant to a particular matter. Once the AI has reached a threshold of confidence in its ability to predict document relevance (or privilege), it can then automatically take steps to speed up human review by suggesting document codes or prioritizing documents for review—or alternatively it can simply apply those labels to the remaining corpus of ESI.

Leveraging AI Earlier in the EDRM

For the past 15 years, e-discovery software has attempted to solve a fundamental problem for legal professionals—namely, it costs too much and takes too long to get to the facts of a given legal matter. Despite the efficiencies it has achieved, growing data volumes, and the high cost of document review, have made cheap, fast, and defensible e-discovery a proverbial holy grail, just beyond the grasp of in-house legal teams and law firms.

However, as deepening integrations have increased visibility into data sources prior to collection, leveraging AI during early case assessment (ECA) has become feasible. AI can apply data mining techniques to vast bodies of data—not just ESI, but also custodian identities and relationships—before any data has been collected. By examining relationships between concepts and existing custodians, AI can suggest additional custodians who should be interviewed and/or placed on legal hold and new keywords and search terms to find relevant ESI. This ability to narrow down the focus on truly relevant custodians and concepts, without sacrificing defensibility, can yield significant downstream savings by reducing data volumes sent for review, as well as by allowing legal teams to set case strategy earlier.

AI as Orchestrator

AI facilitates e-discovery by playing a number of roles in the process: curator, advisor, and orchestrator. Both curator and advisor roles are familiar to e-discovery professionals. AI can recommend documents for deeper review (much like Netflix recommends a new movie or TV show), or it can advise a project manager on scoping custodian lists or collection criteria (as it can suggest a response to a text message or email). But newer AIs can also function as an orchestrator of the entire e-discovery process, learning from past actions and results, and coordinating tasks across multiple channels.

For example, Uber’s technology platform orchestrates users’ entire trips, including pricing, nearest drivers, fastest routes, and estimated wait and arrival times. Similarly, AI can orchestrate the entire e-discovery process. If the relevance rate you got at the end of document review was only 4%, then it would learn that collection criteria can be improved. It can use that insight for future matters to ensure that collection is more targeted and that you select the best reviewers based on historical case results.

For several years, the legal technology marketplace has been hearing about the soon-to-arrive AI revolution—and many have dismissed it as hype. After all, the “robot lawyers” have not taken over. But a closer look at the legal industry reveals more and more reliance on AI to accomplish basic tasks: reviewing documents, managing contracts, predicting case and sentencing outcomes, and even automating tasks like parking ticket disputes. Other industries have shown that AI can fundamentally disrupt old business models, but it takes time and effort. Practitioners and technologists who predict a time when AI discerns the facts of a matter quickly, cheaply, and defensibly will be well served by taking Peter Drucker’s advice to heart: “the best way to predict the future is to create it.”

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