Unsigned decisions (or, per curiam decisions, as they're known in the legal world) have “arguably been abused by courts, by the Supreme Court, and by lower courts,” says William Li, a 2016 computer-science graduate of M.I.T. who has been tracking the high court’s unsigned decisions for years.

“It’s a way of hiding behind a veil of anonymity,” he told me, “a mechanism that kind of removes accountability from them.”

So Li and his colleagues—Pablo Azar, David Larochelle, Phil Hill,

James Cox, Robert Berwick, and Andrew Lo—built an algorithm designed to determine which justice wrote unsigned opinions. (Or which justice’s clerks, as is often the case.) Their work began in 2012, amid rumors that John Roberts, the chief justice, had changed his mind at the last minute about the Affordable Care Act—a move that apparently meant he ended up writing most of the majority opinion after having already written much of the dissent. Li and his colleagues wanted to find out if that theory might be true.

They used a combination of statistical data mining and machine learning to glean each justice’s individual writing style, based on years of their signed opinions. The bot works by analyzing a backlog of opinions and plucking out the words, phrases, and sentence structures that characterize a justice’s unique style. The system then assigns a higher weight to those terms, so it knows what to look for when scanning a per curiam decision. Roberts, they learned, uses the word “pertinent” a lot.

“He seems to tend to start sentences with the word ‘here,’ and end sentences with ‘the first place’—as in, ‘in the first place.’” Li said. “Breyer uses, ‘in respect to.’ For Antonin Scalia, one predictive [word] was ‘utterly,’ and starting the sentence off with ‘of course.’ It does seem like there are these kinds of different writing signatures that exist.”

Distinct signatures that are detectable to a computer, but barely noticeable to a human. Consider, for example, the differences between key words the bot identified in decisions by justices Ruth Bader Ginsburg and Sonia Sotomayor. Ginsburg often uses the words “notably,” “observed,” and “stated,” while Sotomayor favors “observes,” “heightened,” and “lawsuits.”

To test the accuracy of the algorithm’s findings, Li and his colleagues showed it 117 signed opinions (so they knew the correct answer) but withheld authorship. The bot correctly guessed who wrote 95 of them—meaning it was right 81 percent of the time. As for the question of Roberts’s authorship in the Obamacare decision, the computer determined that Roberts almost certainly wrote the majority and Scalia almost certainly wrote the dissent.

Li called the accuracy of his model “gratifying,” but not totally surprising—in part because he expected it to work. “Taking the text of any kind of document and trying to predict a known set of authors—this kind of work actually goes back to the 1960s,” he said.