Katz on Quantitative Legal Prediction

Professor Dr. Daniel Martin Katz of the Michigan State University College of Law and the ReInvent Law Laboratory has published Quantitative Legal Prediction – or – How I Learned to Stop Worrying and Start Preparing for the Data Driven Future of the Legal Services Industry, forthcoming in Emory Law Journal.

Here is the abstract:

Do I Have a Case? What is Our Likely Exposure? How much is this Going to Cost? What will happen if we leave this particular provision out of this contract? How can we best staff this particular legal matter? These are core questions asked by sophisticated clients such as general counsels as well as consumers at the retail level. Whether generated by a mental model or a sophisticated algorithm, prediction is a core component of the guidance that lawyers offer. Indeed, it is by generating informed answers to these types of questions that many lawyers earn their respective wage.

Every single day lawyers and law firms are providing predictions to their clients regarding their prospects in litigation and the cost associated with its pursuit (defense). How are these predictions being generated? Precisely what data or model is being leveraged? Could a subset of these predictions be improved by access to outcome data in a large number of ‘similar’ cases. Simply put, the answer is yes. Quantitative legal prediction already plays a significant role in certain practice areas and this role is likely increase as greater access to appropriate legal data becomes available.

This article is dedicated to highlighting the coming age of Quantitative Legal Prediction with hopes that practicing lawyers, law students and law schools will take heed and prepare to survive (thrive) in this new ordering. Simply put, most lawyers, law schools and law students are going to have to do more to prepare for the data driven future of this industry. In other words, welcome to Law’s Information Revolution and yeah – there is going to be math on the exam.

Click here for slides from the presentation version of the paper, Quantitative Legal Prediction.

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