Algorithm predicts US Supreme Court decisions 70% of time | Ars Technica
A legal scholar says he and colleagues have developed an algorithm that can predict, with 70 percent accuracy, whether the US Supreme Court will uphold or reverse the lower-court decision before it.
"Using only data available prior to the date of decision, our model correctly identifies 69.7 percent of the Court’s overall affirm and reverse decisions and correctly forecasts 70.9% of the votes of individual justices across 7,700 cases and more than 68,000 justice votes," Josh Blackman, a South Texas College of Law scholar, wrote on his blog Tuesday.
"Our model generates many randomized decision trees that try to predict the outcome of the cases, with different variables receiving different weights. This is known as the “extremely randomized trees” method," he said. "Then, the model compares the predictions of the trees to what actually happened, and learns what works, and what doesn’t. This process is repeated… many, many times, to calculate the weights that should be afforded to different variables. In the end, the model creates a general model to predict all cases across all courts."
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