Last summer, John Quinn, founder of Quinn Emanuel, hosted a podcast with a former Biglaw attorney turned legaltech entrepreneur, Dan Rabinowitz. They discussed how Dan’s new AI-driven software, Pre/Dicta, correctly predicted the outcomes of motions to dismiss 85% of the time, using publicly available data points and a case number. Fast forward to May of this year, when it was reported that Quinn Emanuel “will be integrating Pre/Dicta’s predictive analytics tool suite into their litigation workflow” firmwide. It is hard to think of a more ringing endorsement of the value of a new software tool for the legal market than its adoption by one of the country’s most profitable law firms. I was intrigued to learn more about Pre/Dicta’s capabilities, so I am very pleased to share with this audience a written interview that I was fortunate to conduct with Dan about his company and vision.
First, some background about Dan and Pre/Dicta. Prior to founding Pre/Dicta, Dan practiced as an associate in Sidley Austin LLP’s Supreme Court & Appellate and Mass Tort Litigation groups. Later, he served as a trial attorney for the U.S. Department of Justice and the general counsel to a Washington, D.C.-based data science company, and associate general counsel, chief privacy officer, and the director of fraud analytics for WellPoint Military Care.
Dan’s new venture, Pre/Dicta, uses AI and machine learning to forecast outcomes and timelines for case-critical motion practice in civil suits. It is the only commercially available predictive AI for litigation, marking a major leap from the basic data aggregations available through other analytics platforms to Pre/Dicta’s projections, which are specific to the case at hand and made using algorithmic methodologies that were long ago adopted by other industries.
According to Dan, Pre/Dicta’s assessment focuses on the most influential aspect of the case: the judge. Despite the fact that the application of civil code to any given matter should be purely objective, sophisticated practitioners have long recognized that judges of different backgrounds will reach distinctively different conclusions on the same issue, even when the application of law is identical. Pre/Dicta uses data science and AI to quantify these differences and project their impact on active suits. The AI model categorized and classified the decisions of the entire federal judiciary, converting its historic case load into billions of data points and over 100 sextillion permutations which then form the basis for immensely powerful machine learning models that can identify the patterns of consequence within the data. The result: 85% predictive accuracy for motion to dismiss outcomes, highly accurate forecasts for four other motions, and precise timelines.
Now to the interview. As usual, I have added some brief commentary to Dan’s answer below but have otherwise presented his answer to my first question as he provided it.
Gaston Kroub: What makes Pre/Dicta useful for firms and litigants in a litigation environment where analytics is playing an ever-larger role?
Dan Rabinowitz: Litigation data is becoming an increasingly integral component of the practice of law. Many of the existing tools on the market today are distinctively useful for things like case law research and brief writing. Pre/Dicta doesn’t seek to replace those tools nor dispute their necessity in the legal analytics ecosystem. Our predictive technology fills an otherwise not addressed niche. Take for instance the AmLaw100 partner whose case is assigned to a judge with whom they have no experience. It’s commonplace for them to reach out to their practice group and ask if any partner has litigated before this particular judge or, if they’re concerned about a certain motion, they might use another analytics tool to assess the judge’s average grant rate across their docket. Both methods are fundamentally limited in the lack of objective and specific information, neither account for the particularities of the instant action, and both inevitably introduce flawed assumptions and statistics, leading to erroneous results. Pre/Dicta changes all that by codifying the judiciary into statistically significant data points that can be used for objective assessment of a judge’s impact on a case. Armed with reliable intelligence regarding the judge, law firms are empowered to more confidently advise their clients, take a more strategic and calculated approach to motion practice, increase their settlement leverage, select more favorable venues, and pitch new business more effectively.
GK: As someone whose consulting practice for investors and litigation funders is predicated on accurate handicapping of ongoing cases, it is not hard to see the value proposition that Pre/Dicta offers. Likewise, Dan’s example of how practicing litigators can benefit from Pre/Dicta’s functionality suggests that his service will be an attractive one for litigation firms of all sizes. We have all heard of the power of AI to transform how we go about our professional and personal lives. Pre/Dicta has already started to deliver on that promise in a robust way.
Next week, we will conclude our interview with Dan, focusing on what Pre/Dicta’s value proposition might be for the litigation funding and contingent risk insurance markets, as well as how the software may evolve over time.
Please feel free to send comments or questions to me at gkroub@kskiplaw.com or via Twitter: @gkroub. Any topic suggestions or thoughts are most welcome.
Gaston Kroub lives in Brooklyn and is a founding partner of Kroub, Silbersher & Kolmykov PLLC, an intellectual property litigation boutique, and Markman Advisors LLC, a leading consultancy on patent issues for the investment community. Gaston’s practice focuses on intellectual property litigation and related counseling, with a strong focus on patent matters. You can reach him at gkroub@kskiplaw.com or follow him on Twitter: @gkroub.
