Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?

As the most public component of the Supreme Court's decision-making process, oral argument receives an out-sized share of attention in the popular media. Despite its prominence, however, the basic function and operation of oral argument as an institution remains poorly understood, as political scientists and legal scholars continue to debate even the most fundamental questions about its role. Past study of oral argument has tended to focus on discrete, quantifiable attributes of oral argument, such as the number of questions asked to each advocate, the party of the Justices' appointing president, or the ideological implications of the case on appeal. Such studies allow broad generalizations about oral argument and judicial decision making: Justices tend to vote in accordance with their ideological preferences, and they tend to ask more questions when they are skeptical of a party's position. But they tell us little about the actual goings on at oral argument -- the running dialog between Justice and advocate that is the heart of the institution. This Article fills that void, using machine learning techniques to, for the first time, construct predictive models of judicial decision making based not on oral argument's superficial features or on factors external to oral argument, such as where the case falls on a liberal-conservative spectrum, but on the actual content of the oral argument itself -- the Justices' questions to each side. The resultant models offer an important new window into aspects of oral argument that have long resisted empirical study, including the Justices' individual questioning styles, how each expresses skepticism, and which of the Justices' questions are most central to oral argument dialog.

For the first time, the Supreme Court is considering its opinion on the brief but powerful "26 words that created the internet." Enacted in 1996, Section 230 of the Communications Decency Act immunizes online platforms from liability for anything that is posted on their site by a third party--a protection that allowed the web to bloom by encouraging experimentation and interactivity in its early years. More recently, Section 230 has been the subject of scrutiny as bipartisan critics argue that it provides powerful tech companies with too much cover and too little accountability. The Supreme Court's perspective on the issue was a mystery until this week, when justices heard oral arguments for two cases involving 230. On Tuesday, the Court was asked to consider whether Google is liable for YouTube-recommendation algorithms showing Islamic State videos to users.

This week, David Plotz, Emily Bazelon, and John Dickerson discuss Raphael Warnock beating Herschel Walker, and oral arguments at the Supreme Court in the anti-gay marriage website designer case and the "independent state legislature" election case. Here are some notes and references from this week's show: Fr. James Martin, S.J. for Outreach: "When Is Religious Liberty A Fig Leaf For Homophobia?" Here are this week's chatters: David: Tour Fort DeRussy with David; City Cast Portland has launched; Caitlin Doughty for The New York Times: "If You Want to Give Something Back to Nature, Give Your Body" For this week's Slate Plus bonus segment Emily, David, and John discuss ChatGPT.

Artificial intelligence can predict Supreme Court decisions better than some experts. Decision outcomes included whether the court reversed a lower court's decision and how each justice voted. The model then looked at the features of each case for that year and predicted decision outcomes. "Every time we've kept score, it hasn't been a terribly pretty picture for humans," says the study's lead author, Daniel Katz, a law professor at Illinois Institute of Technology in Chicago.