Towards Just, Fair and Interpretable Methods for Judicial Subset Selection

In many judicial systems -- including the United States courts of appeals, the European Court of Justice, the UK Supreme Court and the Supreme Court of Canada -- a subset of judges is selected from the entire judicial body for each case in order to hear the arguments and decide the judgment. Ideally, the subset selected is representative, i.e., the decision of the subset would match what the decision of the entire judicial body would have been had they all weighed in on the case. Further, the process should be fair in that all judges should have similar workloads, and the selection process should not allow for certain judge's opinions to be silenced or amplified via case assignments. Lastly, in order to be practical and trustworthy, the process should also be interpretable, easy to use, and (if algorithmic) computationally efficient. In this paper, we propose an algorithmic method for the judicial subset selection problem that satisfies all of the above criteria. The method satisfies fairness by design, and we prove that it has optimal representativeness asymptotically for a large range of parameters and under noisy information models about judge opinions -- something no existing methods can provably achieve. We then assess the benefits of our approach empirically by counterfactually comparing against the current practice and recent alternative algorithmic approaches using cases from the United States courts of appeals database.