A general approach for predicting the behavior of the Supreme Court of the United States

  title={A general approach for predicting the behavior of the Supreme Court of the United States},
  author={Daniel Martin Katz and Michael James Bommarito and Josh Blackman},
  journal={PLoS ONE},
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. To do so, we develop a time-evolving random forest classifier that leverages unique feature engineering to predict more than 240,000 justice votes and 28,000 cases outcomes over nearly two centuries (1816-2015). Using only data available prior to decision, our… 

Figures and Tables from this paper

Using Modern Neural Networks to Predict the Decisions of Supreme Court of the United States with State-of-the-Art Accuracy

This paper builds upon the works of Katz, Bommarito and Blackman 2014, who use extremely randomized trees and feature engineering to help in predicting the behaviour of Supreme Court of United States, and trains Deep Neural Networks trained using momentum methods and incorporating the Dropout technique.

Using deep learning to predict outcomes of legal appeals better than human experts: A study with data from Brazilian federal courts

Legal scholars have been trying to predict the outcomes of trials for a long time. In recent years, researchers have been harnessing advancements in machine learning to predict the behavior of

Using machine learning to predict decisions of the European Court of Human Rights

This work investigates how natural language processing tools can be used to analyse texts of the court proceedings in order to automatically predict (future) judicial decisions, and demonstrates that it can achieve a relatively high classification performance when predicting outcomes based only on the surnames of the judges that try the case.

Predicting the Outcome of Judicial Decisions... Preprint

This was seemingly the first study that included such a heuristic of always predicting the most common outcome in the past with which to compare model results, and the higher accuracy achieved by the heuristic highlights the importance of including such a baseline.

Case-level prediction of motion outcomes in civil litigation

This work presents the first ML-based methods to support lawyer and client decision making in real-time for motion filings in civil proceedings and confirms the usefulness of incorporating attorney case-entropy and natural language features from complaint documents.

Predicting United States policy outcomes with Random Forests

This paper analyzes the Gilens dataset using the complementary tools of Random Forest classifiers (RFs), from Machine Learning to present two primary findings, concerning prediction and inference: Holdout test sets can be predicted with approximately 70% balanced accuracy by models that consult only the preferences of rich people and a small number of powerful interest groups, as well as policy area labels.

Predicting Decisions of the Philippine Supreme Court Using Natural Language Processing and Machine Learning

This is the first systematic study in predicting Philippine Supreme Court decisions based purely on textual content and the best result obtained is 59% on the topic datasets using a random forest classifier.

Predicting Indian Supreme Court Judgments, Decisions, Or Appeals

This paper introduces the newly developed ML-enabled legal prediction model and its operational prototype, eLegPredict; which successfully predicts the Indian supreme court decisions.

Optimizing the Efficiency of Machine Learning Techniques

This work investigates and applies more efficient sets of predictors variables with a machine learning classifier over a large size legal dataset for court judgment prediction and depicts that incorporation of feature selection technique has significantly improved the performance of predictive classifier.

Machine Prediction of Political Polarization from Circuit Court Judgements

Using a unique dataset of the United States courts of appeals opinions, we examine how prose (writing style), precedent (citations to previous cases), and policy (dis-senting votes) are indicative of



Predicting the Behavior of the Supreme Court of the United States: A General Approach

The model is distinctive as it is the first robust, generalized, and fully predictive model of Supreme Court voting behavior offered to date and represents a major advance for the science of quantitative legal prediction.

Justice Blocks and Predictability of U.S. Supreme Court Votes

It is argued that, within this framework, high predictability is a quantitative proxy for stable justice blocks, which probably reflect stable a priori attitudes toward the law and that aggregate court predictability has been significantly lower during Democratic presidencies.

The Supreme Court Forecasting Project: Legal and Political Science Approaches to Predicting Supreme Court Decisionmaking

This Essay reports the results of an interdisciplinary project comparing political science and legal approaches to forecasting Supreme Court decisions. For every argued case during the 2002 Term, we

Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective

The empirical analysis indicates that the formal facts of a case are the most important predictive factor, consistent with the theory of legal realism suggesting that judicial decision-making is significantly affected by the stimulus of the facts.

Statistical Mechanics of the US Supreme Court

It is suggested that simple models, grounded in statistical physics, can capture essential features of collective decision making quantitatively, even in a complex political context.

Law on the Market? Abnormal Stock Returns and Supreme Court Decision-Making

What happens when the Supreme Court of the United States decides a case impacting one or more publicly-traded firms? While many have observed anecdotal evidence linking decisions or oral arguments to

Competing Approaches to Predicting Supreme Court Decision Making

Political scientists and legal academics have long scrutinized the U.S. Supreme Court's work to understand what motivates the justices. Despite significant differences in methodology, both

Law on the Market? Evaluating the Securities Market Impact of Supreme Court Decisions∗

Do judicial decisions affect the securities markets in discernible and perhaps predictable ways? In other words, is there “law on the market” (LOTM)? This is a question that has been raised by

The Law Machine

In a low-rise building in Menlo Park, Calif., just upstairs from a Mexican restaurant and a nail salon, a Stanford University spin-off is crunching data in ways that could shake the foundations of

Assessing Preference Change on the US Supreme Court

The foundation upon which accounts of policy-motivated behavior of Supreme Court justices are built consists of assumptions about the policy preferences of the justices. To date, most scholars have