Keynote: Measure Twice, Cut Once: Solving the Legal Profession’s Biggest Problems Together #ILTACON

2016_ILTACON_logoSession Description: Clients and law firms need to work together to solve the biggest problems facing them both, including the optimal overall data strategy for collection, hoarding, preservation, measurement and metrics; data we should collect today to help answer the questions of tomorrow; leveraging current technologies to improve data measurement and regularization and overall collaboration; measuring the performance of and determining fair value for legal work; how to create appropriate micro-incentives to further innovation; startup technologies being developed that are worthy of consideration; and where we’ll find the staff to manage it all. Together, we’ll measure twice, cut once and start solving legal’s biggest problems.

Speaker: Daniel Katz, Associate Professor of Law, Chicago-Kent College of Law.  Dan is a scientist, technologist and law professor who applies an innovative polytechnic approach to teaching law, meshing litigation and transactional knowledge with emerging software and other efficiency-enhancing technologies to help prepare lawyers for today’s challenging legal job market. His forward-thinking ideas helped earn him acknowledgement among the Fastcase 50, which “recognizes 50 of the smartest, most courageous innovators, techies, visionaries and leaders in the law.” He was also named to the American Bar Association Journal‘s “Legal Rebels,” a prestigious group of change leaders in the legal profession. Professor Katz teaches civil procedure, e-discovery and entrepreneurial lawyering at Chicago-Kent and spearheads new initiatives to teach law students how to leverage technology and entrepreneurship in their future legal careers.

[These are my notes from the International Legal Technology Association’s 2016 Conference. I’m publishing them as soon as possible after the end of a session, so they may contain the occasional typographical or grammatical error. Please excuse those. To the extent I’ve made any editorial comments, I’ve shown those in brackets.]

NOTES:

  • How to build a more perfect supply chain.  This requires that we move more parts of legal practice from the “art” column to the “science” column. This, in turn, requires that we measure our work more rigorously.
  • Economics of the law.
    • What’s a lawyer’s value proposition? What do lawyers solve for? If you get too far from that value proposition, you become irrelevant.
    • the value proposition:
      • help people manage complexity
      • help people manage enterprise risk
    • Lawyers as Complexity Engineers: In the face of growing legal complexity, we have applied greater and greater numbers of human experts to solve problems. (We throw more and more labor at the problem.) This, in turn, creates a huge opportunity for disruption. Do we really need all these people? Can some of what they do be done by a machine. Better yet, can we remove some of the complexity?
    • Where are the large-scale complexity-filled opportunities in law? Banks! They do very complex transactions and need increasingly efficient ways to cope with the legal complexity of their transactions.
    • Paul Lippe’s insight on three types of lawyers:
      • Mediocre lawyers play whack-a-mole, seeing legal risk around every corner
      • Clever lawyers find solutions to those legal risks
      • Great lawyers help design systems that can balance risk and then price risk correctly.
    • Why do we have law firms?
      • Every client has to decide whether to make or buy.
        • Law firms help offset peak load by helping law departments from having to staff up when unusal issues arise.
        • Firms provide high value by providing expertise that is rarely used or hard to acquire
    • The problem of agency costs:
      • If I am the principal and hire an agent to act on my behalf, the agent often has more information than the principal. This creates agency cost. Therefore, this relationship is always slightly antagonistic.
  • Moving from Artisinal to Industrial.
    • Across many industries, we are moving things from the artisan column to the industrial column. This requires standardizing and measuring business processes. The result is reliable and repeatable.
    • This does not need to mean a loss of quality. We do not need to move from being Sal’s artisanal pizza to Dominos. We need to produce artisinal- quality pizza with the economies of industrial processes.
  • Creating a Data-Driven Enterprise.
    • Our understanding of our processes is imperfect. We rarely appreciate how complicated they really are. We rarely see how simple they really should be.
    • The first step is process mapping. When process mapping, don’t make it so granular that it turns into a #ridiculogram.
    • Don’t merely aim for understanding the average cost/effort. Focus on the variability. Understand what moves things out of the left tail and into the right tail.
    • Transparency is the glue that builds relationships. But in the legal industry, we hate transparency. If we want real change in the industry, the transparency has to work both ways. Both law firms and clients must share their data. Only then will we have a clearer understanding of actual opportunities and risks.
    • Data-driven outcomes = using data to underwrite legal risk.
    • Data-driven transactional work = using data to determine the value of particular negotiating and drafting approaches. If we don’t know how much risk is being avoided or created, how can we choose the right approach?
    • We need a better understanding of the actual drivers of risk. A mediocre lawyer sees risk everywhere. A great lawyer has the data to explain the actual risks.
    • Human are good at pattern detection. A high-frequency trader may take only 60 seconds to identify a pattern, but in that minute the arbitrage opportunity will disappear because computers are faster.
    • Predicting case outcomes using data. When it comes to forecasting, there are only 3 ways to predict: experts, crowdsourcing, and algorithms.
      • However, “experts” don’t really need to be expert in law in order to predict well. Take the example of Jacob Berlove, an actuary who lives in Queens, New York, who is one of the best predictors of supreme court case outcomes.
      • Yet there is something that can outperform a high-performing individual. A Human + a Machine always outperforms either a Human OR a Machine.
  • Legal Analytics & Machine Learning as a Service (MLaaS). Law is a relatively small vertical. And it has a great diversity of expertise. Therefore, it is unlikely that the big players such as IBM will focus on the legal vertical. However, we can take IBM’s general offerings such as Watson and conquer the last mile, which is to figure out how to adapt it to the legal industry.
  • Fin(Legal)Tech.
    • If you are offering alternative fee arrangements, you are self-insuring because you are assuming the risk. However, most firms do so without the necessary data or models. Crazy!
    • Fintech is about removing socially meaningless friction and then characterizing and pricing exotic risks. Once we understand the data under the legal system and use that data to characterize and price legal risk, we will create Fin(Legal)Tech.
  • Practical Steps You can Take:
    • Improve your early case assessment. Collect and understand the data your have in your firm regarding litigation cases. Then build a predictive model.
    • Improve your transactional predictions. Collect markups on every deal document. Understand what they are and why they are done. Then you can predict the next negotiating/drafting tactic of opposing counsel. And you can assess the costs of every move.
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