ArticlePloS one2017
A general approach for predicting the behavior of the Supreme Court of the United States.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of automated journalism scholarship: guidelines and suggestions for future research.Open research Europe · 2021Pooled it
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- Discovering significant topics from legal decisions with selective inference.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2024Article
- Machine learning predicts which rivers, streams, and wetlands the Clean Water Act regulates.Science (New York, N.Y.) · 2024Article
- Advanced neural network-based model for predicting court decisions on child custody.PeerJ. Computer science · 2024Article
- Predicting citations in Dutch case law with natural language processing.Artificial intelligence and law · 2024Article
- Perceptions of Justice By Algorithms.Artificial intelligence and law · 2023Article
- Towards a simple mathematical model for the legal concept of balancing of interests.Artificial intelligence and law · 2023Article
- AI and professional liability assessment in healthcare. A revolution in legal medicine?Frontiers in medicine · 2023Review
- RegBR: A novel Brazilian government framework to classify and analyze industry-specific regulations.PloS one · 2022Article
- Using deep learning to predict outcomes of legal appeals better than human experts: A study with data from Brazilian federal courts.PloS one · 2022Article
- The dynamics of ideology drift among U.S. Supreme Court justices: A functional data analysis.PloS one · 2022Article
- A Computational Intelligence Model for Legal Prediction and Decision Support.Computational intelligence and neuroscience · 2022Article
- Predicting Brazilian Court Decisions.PeerJ. Computer science · 2022Article
- LawRec: Automatic Recommendation of Legal Provisions Based on Legal Text Analysis.Computational intelligence and neuroscience · 2022Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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. 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 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 important advance for the science of quantitative legal prediction and portend a range of other potential applications.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.