A General Approach for Predicting the Behavior of the Supreme Court of the United States (Paper Version 2.01) (Katz, Bommarito & Blackman)

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Long time coming for us but here is Version 2.01 of our #SCOTUS Paper …

We have added three times the number years to the prediction model and now predict out-of-sample nearly two centuries of historical decisions (1816-2015). Then, we compare our results to three separate null models (including one which leverages in-sample information).

Here is the abstract:  Building on developments in machine learning and prior work in the science of judicial prediction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context. Our model leverages the random forest method together with unique feature engineering to predict nearly two centuries of historical decisions (1816-2015). Using only data available prior to decision, our model outperforms null (baseline) models at both the justice and case level under both parametric and non-parametric tests. Over nearly two centuries, we achieve 70.2% accuracy at the case outcome level and 71.9% at the justice vote level. More recently, over the past century, we outperform an in-sample optimized null model by nearly 5%. Our performance is consistent with, and improves on the general level of prediction demonstrated by prior work; however, our model is distinctive because it can be applied out-of-sample to the entire past and future of the Court, not a single term. Our results represent an advance for the science of quantitative legal prediction and portend a range of other potential applications.

Noise: How to Overcome the High, Hidden Cost of Inconsistent Decision Making (via Harvard Business Review)

screen-shot-2016-09-14-at-9-51-44-amFrom the article:  “The prevalence of noise has been demonstrated in several studies. Academic researchers have repeatedly confirmed that professionals often contradict their own prior judgments when given the same data on different occasions. For instance, when software developers were asked on two separate days to estimate the completion time for a given task, the hours they projected differed by 71%, on average. When pathologists made two assessments of the severity of biopsy results, the correlation between their ratings was only .61 (out of a perfect 1.0), indicating that they made inconsistent diagnoses quite frequently. Judgments made by different people are even more likely to diverge. Research has confirmed that in many tasks, experts’ decisions are highly variable: valuing stocks, appraising real estate,sentencing criminals, evaluating job performance, auditing financial statements, and more. The unavoidable conclusion is that professionals often make decisions that deviate significantly from those of their peers, from their own prior decisions, and from rules that they themselves claim to follow.”

Suffice to say we at LexPredict agree.  Indeed, building from our work on Fantasy SCOTUS where our expert crowd outperforms any known single alternative (including the highest ranked Fantasy SCOTUS player), we have recently launched LexSemble (our configurable crowdsourcing platform) in order to help legal and other related organizations make better decisions (in transactions, litigation, regulatory matters, etc.).

We are working to pilot with a number of industry partners interested in applying underwriting techniques to more rigorously support their decision making.  This is also an example of what we have been calling Fin(Legal)Tech (the financialization of law).  If you want to learn more please sign up for our Fin(Legal)Tech conference coming on November 4th in Chicago) (tickets are free but space is limited).

Quantified Law Primer & Agile Legal Project Management Workshop

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In conjunction with Janders Dean International, and SeyfarthLean Consulting we are excited to offer two, half-day educational sessions on July 13th 2016.

Quantified Law Primer (Morning SESSION)
Instructors: Daniel Martin Katz (Chicago-Kent College of Law), Karl Haraldsson (Janders Dean), and Andrew Baker (Janders Dean)

Agile LPM Workshop (Afternoon SESSION)
Instructors: Kim Craig (SeyfarthLean Consulting), Andrew Baker (Janders Dean), and Justin North (Janders Dean)

These workshops are to be held in conjunction with the Chicago Kent- Janders Dean Legal Horizon Conference on July 14th in Chicago.  The Legal Horizon event features more than 20+ speakers in a single day, single stage event.

The Three Forms of (Legal) Prediction: Experts, Crowds + Algorithms (Updated Version of Presentation)

Legal Tech Asia – Hong Kong 2016

It was a great pleasure to deliver the opening keynote at LegalTech Asia 2016 here at the JW Marriott in Hong Kong.  The event draws leading legal professionals from across the Asia-Pacific region. It was a great to connect with everyone as part of a global conversation directed toward moving the profession forward!