Emerging Mass Arbitrations May Be Ideal Proving Ground for AI

Based on news coverage, you might think artificial intelligence is coming for your legal job. AI so far has racked up impressively high 90% scores on the LSAT and the Uniform Bar Exam, and passed several law school exams from the University of Minnesota.

In time, AI is expected to slash the time attorneys spend on many tasks, including document review, legal research, drafting memos, briefs, correspondence, deposition preparation, and contract analysis and management.

Another specific deployment of AI is the role, if any, it might take in the emerging area of mass arbitrations.

The two areas seem to go together naturally. Mass arbitration is considered high-risk because of the potential multiplying power of large numbers of pattern claims. AI promises to help identify, capture, analyze, and “think” about large data sets. Accordingly, defense of mass claims seems a good place to road-test AI. Consider these possible uses.

Arbitrator Selection

Arbitrators hold your clients’ fates in their hands, but who are they and how might they decide? AI seems tailor-made to answer these questions. Ferreting out arbitrator information sometimes requires serious investigative skills, and relying on your standard all-office “ISO” seems woefully inadequate for a complex mass arbitration.

Clause Review

Mass arbitration is still in its relative infancy, and smart companies have tried all kinds of strategies to craft legally sound arbitration clauses that meet the goals of the company. Consider harnessing AI to scour publicly facing consumer arbitration clauses to keep on top of best practices in arbitration clause drafting.

Solicitation Sleuthing

Robo-powered and potentially misleading client solicitations are the first seeds of many mass arbitration harvests. Indiscriminate solicitations yield dubious client/lawyer relationships and a bumper crop of names of people who may or may not be within the scope of the purported mass arbitration—and may or may not understand that by “clicking yes” they were consenting to initiate any type of legal action.

Knowing who and how many professional plaintiffs in your claimant pool spend their days clicking “sign me up” to a slew of sometimes overlapping actions is useful as part of an overall claims analysis. As with other big data roundups, AI can help.

Research Optimization

No more late nights sifting through head notes and case text? Not so fast. AI mimics the human voice, but sometimes it generates false results that are nothing but pure fabrication. Imagine citing Smith v. Jones only to find out it’s a figment of the AI mind. No question, AI can—and already is—sharpening legal research by combing vast data sets for relevant precedent. And just as AI might cast its net too wide, it also may return too-narrow results because it may lack that human ability to reason by analogy, extension, or implication like the best litigators do. AI may help you find more faster, but you still must read it and apply that ineffably human touch of persuasion.

Pattern Responses

Legal-specific AI may help standardize and automate certain responsive pleadings once a master template is set. It also can be used to comb existing repositories of information internally and externally to identify useful exemplars. AI seems particularly useful for the mechanical adaptation task once the hard human thinking of setting the initial strategy and master response is set.

Document Dumps

At first blush, AI would seem an attractive replacement for hours spent in document review, but the learning feature of AI must not inadvertently result in unanticipated disclosures. AI is notoriously promiscuous when it comes to information. If you are tempted to harness AI for document review tasks, consider an AI tool tailored to the legal profession that doesn’t “hallucinate” imaginary document results or spill your clients’ secrets.

While an AI replacement won’t likely replace flesh-and-blood arbitrators anytime soon, AI has its uses. AI seems primed to supercharge efficiency in data collection and review in certain hours-dense tasks, but quality control is key.

Don’t just give over your practice to AI the way you might surrender the wheel to a self-driving car—your malpractice insurance carrier likely agrees.

And while you ponder how you might deploy AI to collect, sift through, and analyze large caches of data, ask yourself: Did a lawyer with decades of experience suggest these possible mass arbitration AI deployments or did an AI chatbot, trained by massive datasets, produce these snippets of wisdom?

This article does not necessarily reflect the opinion of Bloomberg Industry Group, Inc., the publisher of Bloomberg Law and Bloomberg Tax, or its owners.

Author Information

Ann Marie Mortimer is managing partner of the Los Angeles office of Hunton Andrews Kurth and leads the firm’s commercial litigation practice. Her practice focuses on mass and class cases, including mass arbitration.