Recently I taught a voluntary, thirty-minute, we’ll-give-you-a-takeaway-lunch-if-you-come workshop for law students on legal analytics. What is the best way to teach these sessions? I took a different approach than I use when teaching a credit-bearing class and took a high-level view, less skills-focused approach. After all, students are attending on their own initiative during their lunch hour, so I don’t want to work them too hard! It was also meant to be an introduction for beginners. For a lesson with real-world exercises, I highly recommend Cassie Rae Walker’s RIPS post, Class Exercise: Turning Research into a Deliverable Using Analytics.
I organized the workshop around three simple “big picture” questions:
- What has driven the growth of legal analytics over the past decade?
- How are lawyers using legal analytics?
- What are the strengths and pitfalls of legal analytics?
Part 1: What has driven the growth of legal analytics over the past decade?
I thought students would be surprised and interested to learn that the idea of legal analytics can be traced at least as far back as an 1897 speech by Oliver Wendell Holmes Jr. that was published as The Path of Law, 10 Harv. L. Rev. 457 (1897). “The prophecies of what the courts will do in fact, and nothing more pretentious, are what I mean by the law.” Id. at 461. He also said “the man of the future is the man of statistics. . .” Id. at 469. His vision is finally becoming a reality (though it’s interesting to consider whether the analytics of today fulfill Holmes’s vision) because of e-filing. The data creating by e-filing is the oil of legal analytics! Lawyers have been undergoing a culture shift in awakening to the value of the insights hidden among all that data. We also discussed other sources of data (like internal firm data), issues with data quality, and data cleaning.
Part 2: How are lawyers using legal analytics?
Legal analytics can be divided into two areas: the practice of law and the business of law (though that line can be blurry). Personally I find the business of law applications to be more compelling. I listed examples of how legal analytics is used in both areas and provided a couple of demonstrations. I discussed which uses have had the most adoption by attorneys using the results from Lex Machina’s 2021 Adoption of Legal Analytics survey. We looked at marketing materials for legal analytics tools and briefly discussed their messaging.
Part 3: What are the strengths and pitfalls of legal analytics?
Discussing advertising led us into an assessment of whether analytics companies deliver on their promises. I gave my opinion that analytics are excellent in evaluating potential damages in a case, forecasting the time and expense for litigating a matter, evaluating firms, attorneys, and expert witnesses, and determining how to price work and deploy firm resources (for which firms need to leverage internal data). Students were interested in how analytics could be used to make a sales pitch to a new client.
In terms of pitfalls, using legal analytics risks amplifying bias in the system, like algorithms in legal research. Analytics are backward looking, so what is their proper role in developing the law in the future? If a judge denies a motion in 99 out of 100 cases, does that mean an attorney shouldn’t file a similar motion or is unjustified if they do? I can see the benefits in using analytics in areas where we have a lot of data and we want to improve transparency and consistency, like criminal sentencing. But what about areas with limited case law? I would rather craft my own argument than have an algorithm put words in my mouth and tell me exactly what will persuade a particular judge. It seems manipulative to me, but maybe I’m old fashioned. Finally, do analytics really give attorneys who can afford them an unfair advantage? The analytics purveyors say they give you an advantage (they would argue a fair one).
We could have spent a lot more time discussing these questions, but I hope the workshop provided a foundation for students to begin thinking about how they would/should use analytics. It would be great to have a follow-up session with skills practice, but I wonder how much student interest there would be?