Rewriting Judicial Recusal Rules with Big Data
37 Pages Posted: 6 Aug 2020
Date Written: July 1, 2020
Big data affects the personal and professional life of every judge. A judge’s travel time to work, creditworthiness, and chances of an IRS audit all depend on predictive algorithms interpreting big data. A client’s choice of counsel, the precise wording of a litigant’s motion, and the composition of the jury may be dictated by analytics. Touted as a means of bringing objectivity to judicial decision-making, judges have employed big data to determine sentences and to set the amount of restitution in class action cases. Unfortunately, the legal profession and big data proponents have ignored one perplexing problem begging for a big data solution — the arbitrary and inconsistent manner in which courts determine judicial recusal issues.
Every jurisdiction disqualifies a judge when the fully informed, reasonable, lay observer concludes that the judge’s “impartiality might reasonably be questioned.” Created by the American Bar Association in 1972 to bring uniformity and consistency to the disqualification process, this “objective” test has been a dismal failure. The ABA’s goal, however, can be realized by infusing data analytics into the disqualification decision-making process.
Part I of this Article identifies the serious shortcomings of an appearance-based disqualification standard. Part II explains how analysis of big data can correct the theoretical and practical problems plaguing the “might reasonably be questioned” standard. Part III applies the big data derived model to one type of disqualification motion — motions seeking a judge’s removal from a case because of contributions made to the judge’s election campaign by litigants, lawyers, or others connected with the litigation.
Keywords: Judicial ethics, judicial disqualification, recusal, “impartiality might reasonably be questioned,” artificial intelligence, big data, Model Code of Judicial Conduct, analytics, judicial campaign contributions
JEL Classification: K10, K40, K41
Suggested Citation: Suggested Citation