Dr. Zev J. Eigen is the Global Director of Data Analytics at Littler Mendelson, (the world’s largest management-side labor and employment law firm), where he heads up the Firm’s Big Data Initiative. And is the Cofounder/CSO of Syndio. He combines his expertise in data science, complex data analytics and social scientific research with more than 15 years of experience as a practitioner and professor in labor and employment law. His unique and creative approach to applied data science in the HR space has earned him numerous awards and recognition.
For Zev’s work developing artificial intelligence solutions for employers, the Financial Times recognized Zev as one of the 10 most innovative lawyers. He is also a FastCase50 Innovator. He is a frequent speaker at conferences and events around the globe and has been cited by the Wall Street Journal, the New York Times, Forbes, Bloomberg News and others.
Zev has authored more than a dozen scholarly articles and book chapters, and his work has appeared in the Journal of Legal Studies, NYU Law Review, Industrial and Labor Relations Review among others. He holds a PhD from MIT (’09), a JD from Cornell Law School (’99) and BS from Cornell University in Industrial & Labor Relations (’96).
I work with lawyers and clients to build models that solve business and legal problems. Sometimes those solutions are data-driven, sometimes they involve AI and machine learning, and other times they involve proper statistics and research methods. It spans the gamut. I strive to identify solutions that are workable, sustainable and cost-effective. I don’t strive to use complicated methods for the sake of using them.
I took a syllogistic reasoning course as an undergraduate, where I was already very interested in statistics and labor economics. I did well in the course and asked the professor what someone should do if they like syllogistic reasoning and logic and he suggested I go into computer programming. So I did. That was 1997, I think.
They help the way adding tools in a tool kit helps. They offer better ways of diagnosing and solving business and legal decisions. The way that people and organizations address problems can almost always fit somewhere on this scale: (1) intuition/ biological response (2) subject matter expertise (3) statistics (4) data science and (5) autonomous algorithmic decision-making. Most decisions at law firms are made based on (1) and (2). Occasionally, (3), and rarely, if ever 4. Forget about (5) for the moment.
Some law schools are rushing to figure this out and realizing they are way behind. Some have tried to appear innovative even though they are not. Others are doing a great job of genuinely helping their students to see potential beyond the standard curriculum. I love what Tanina Rostain is doing at Georgetown, and I love what Charlotte Alexander is doing at Georgia State. Those are two great examples.
Law firms are in a similar boat. Firms are chock full of lawyers. Lawyers know how to lawyer. Many went into law because they are the polar opposite of innovative and entrepreneurial. They wanted to be a part of a profession, not so much a business. So, the leadership of law firms recognize that they need to appear/ be innovative because their clients want it and they fear being left behind, but they are just starting to realize what that entails. Ironically (and predictably), many law firms wait for their competition to innovate so they can feel safe copying them.
I don’t focus on “technology” or “innovation.” I focus on solving problems. I’ll offer three examples of what could be considered innovative approaches to solving problems on which I’ve worked: First, I’ve built machine learning systems for clients interested in predicting where legal risk will materialize across many locations. Second, I’ve built AI systems that predict whether a new-hire is likely to commit a crime in the next 6–8 months. The “Cherry Tree Data Science” system enables employers to safely and reliably hire applicants with criminal records who are no more risky than applicants without criminal records. Third, in conjunction with Syndio, I’ve used relational data (measures of how connected people are to each other on networks in organizations) to reduce gender and racial bias in organizational decision-making, and to improve diversity and inclusion.
No. Just kidding. We are very focused on the three pillars of successful diversity and inclusion initiatives: (1) Finding and hiring talent, and looking in different sources to reduce homophily and replication of existing distributions; (2) measuring inclusion and improving it where it is lacking — not just admiring the problem, but doing something about it with relational data; and (3) using technology to help companies close the gender (and racial) pay gap.
I see a bifurcation in the market occurring now, with a move towards flexible specialization. There will be law firms, but I think that the role of lawyers that are not doing bespoke white-shoe work will become folded into other service and software offerings. With mounting pressures from the alternative law space, greater demand for better work at lower costs will require revamping the structure of how legal services are provided.
One of the biggest concerns people have is the pipeline of qualified candidates coming out of law schools. As the value proposition of going to law school continues to shift in favor of alternative career paths for smart, talented people, the need for AI and non-traditional systems to fill that void will continue to grow.
“Firms established today should rely heavily on AI solutions like ROSS and empower scaling work with fewer people… Incentivize attorneys to automate, innovate, scale, and offer solutions to problems clients are facing.”
AI is a tool in the tool-kit. As it continues to improve, and we continue to see systems developing with greater democratized access and appeal, there will be more adoption and greater normalization of the approach. I see cohort effects here. People who regard law as a profession first tend to be less accepting of what they regard as an intrusion on their subject matter domain. People who regard law as a business first, are more accepting of AI and regard it less as an intrusion and are curious about how they can use it to improve their work.
So many suboptimal decisions are made at organizations because they are based on instinct, or what people “know” from anecdotal experience. They know something to be true because they rely on their limited experiences. For instance, if you were hiring someone for the job of president of the United States, you could erroneously conclude that you would need a man, and probably a white man, given the past people in that job. That’s what we mean by the “dark.”
Syndio offers software solutions that gather, analyze and make data actionable for organizational decision makers and leaders. For instance, we offer a software solution for executives trying to identify the most influential talent at their organization derived from quantitative and qualitative relational data we gather on their behalves.
We use tried and valid social scientific methods and ensure that the output of the analytics is presented in an actionable way to maximize their value. The data Syndio gathers and analyzes are extremely powerful because they measure the connectivity across individuals in organizations. Those data are more predictive than other static forms of data, like “attribute” data that reside on individuals (e.g. highest educational attainment, or years of experience).
I taught a class called, “Power, Status and Negotiation,” which offers a social exchange theoretical approach to traditional negotiations. Essentially, it uses Social Network Analysis as a basis for informing negotiation theory. The course included practical simulation exercises. It was a fun and interesting experience and I was glad I got to meet so many interesting students, many of whom were only tangentially interested in law, it seemed.
Use software to scale and automate. The old model of practicing law is based on the simple formula of hiring more lawyers to bill more time to make more profit. I think new firms established today should rely heavily on AI solutions like ROSS and empower scaling work with fewer people done with more of an eye towards automation. Forget about hourly work. Forget about hiring lawyers and incentivizing them to bill more time. Incentivize attorneys to automate, innovate, scale, and offer solutions to problems clients are facing. That will optimize subject matter expertise and give clients better results without the burdensome overhead of old-school law firm models.
I have a really long list but here are two: