Lifting the American Supreme Court Veil: Identifying Authorship in Unsigned Opinions

The Supreme Court of the United States (SCOTUS) issues 10-15 % of its opinions unsigned, concealing authorship. Traditionally, unveiling authors required the posthumous release of Justices' personal papers. We trained our AI algorithm to achieve real-Time authorship probabilistic identification, encompassing 17 Justices and 4,069 opinions from 1994 to 2024. Our algorithm identified the likely authors of the March 2024 Trump v. Anderson case, which enabled Donald Trump to run for office. Moreover, our algorithm unveiled the likely authorship in significant unsigned COVID-19 era cases, estimated with high probability individual parts of the joint dissent in the Obamacare Case (2012), and discerned the likely authors of the landmark cases of Bush v. Gore (2000). Applications range from legal research to decoding SCOTUS internal dynamics. Compared to prior methods, our study demonstrates a substantially higher accuracy rate of 91 per cent over a much longer period of time, offering timely insights into the nuances of SCOTUS decision-making. To facilitate further research, we provide a public web server at https://raminass.github.io/SCOTUS_AI/.