The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
A general approach for predicting the behavior of the Supreme Court of the United States
Open accessAbstract
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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Classification And Regression Trees
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Iteratively Questioning and Answering for Interpretable Legal Judgment Prediction
Zhong, H., Wang, Y., Tu, C., Zhang, T., Liu, Z., & Sun, M. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i01.5479 · Cited by 113
LexGLUE: A Benchmark Dataset for Legal Language Understanding in English
Chalkidis, I., Jana, A., Hartung, D., Bommarito, M. J., Androutsopoulos, I., Katz, D. M., & Aletras, N. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3936759 · Cited by 111
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Semi-Supervised Methods for Explainable Legal Prediction
Branting, K., Weiss, B., Brown, B., Pfeifer, C., Chakraborty, A., Ferro, L., Pfaff, M., & Yeh, A. (2019) · Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law · doi:10.1145/3322640.3326723 · Cited by 43
Metadata
- Authors
- Katz, D. M., Bommarito, M. J., & Blackman, J.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2017-04-12
- Volume
- 12
- Issue
- 4
- Pages
- e0174698
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W3122334902
- Cited by
- 528
- References
- 31
- FWCI
- 206.93
- Type
- journal article
- Language
- en
- Open access
- gold
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