From WBUR Cognoscenti
Within legal circles, the mystery of “Whodunnit?” has increasingly become “Who wrote it?” as courts, including the U.S. Supreme Court, keep issuing opinions without divulging who actually authored them. Since 2005, for example, the Roberts Court has disposed of at least 65 cases through unsigned per curiam opinions. Many cases also came with unsigned concurring or dissenting opinions.
We place a high value on transparency in our democracy, and that should certainly apply to Supreme Court justices, who, after all, are already protected by lifetime tenure. Obscuring authorship removes the sense of judicial accountability, making it harder for experts and the public alike to understand how important issues were resolved and the reasoning that led to these decisions, especially in controversial cases. We’ve all heard the charge that judges are legislating from the bench — but assessing that claim requires, at the least, the ability to link opinions to individual decision makers.
This is why my colleagues and I decided to apply machine learning algorithms to predict the authorship of opinions that are unsigned or whose attribution is disputed — we wanted to see if technology could help uncloak judicial anonymity. The challenging nature of parsing legal text algorithmically required broad collaboration across several different disciplines and institutions including computer science and engineering at MIT, the Berkman Center for Internet & Society at Harvard, and a practicing lawyer. [Full credits below]
Read the full post at WBUR Cognoscenti.
Andrew W. Lo is the Charles E. and Susan T. Harris Professor, a Professor of Finance, and the Director of the Laboratory for Financial Engineering at the MIT Sloan School of Management.