Artificial intelligence is being utilized in many domains as of late, and the legal system is no exception. However, as it stands now, the number of well-annotated datasets pertaining to legal documents from the Supreme Court of the United States (SCOTUS) is very limited for public use. Even though the Supreme Court rulings are public domain knowledge, trying to do meaningful work with them becomes a much greater task due to the need to manually gather and process that data from scratch each time. Hence, our goal is to create a high-quality dataset of SCOTUS court cases so that they may be readily used in natural language processing (NLP) research and other data-driven applications. Additionally, recent advances in NLP provide us with the tools to build predictive models that can be used to reveal patterns that influence court decisions. By using advanced NLP algorithms to analyze previous court cases, the trained models are able to predict and classify a court's judgment given the case's facts from the plaintiff and the defendant in textual format; in other words, the model is emulating a human jury by generating a final verdict.

When Facebook announced a new dating feature at its annual developer conference this week, it drew quick comparisons to existing apps like Tinder and Bumble. But the social network's matchmaking service, simply called Dating, most closely resembles another, lesser known dating app: Hinge. Facebook hasn't yet begun to test Dating, but the demo version touted on stage by CEO Mark Zuckerberg and chief product officer Chris Cox looks nearly identical to Hinge. This isn't the first time Facebook has ripped off a competitor; Instagram famously lifted Stories from Snapchat in 2016. And as in previous cases, Hinge probably doesn't have much recourse to stop them.

Is it possible to predict the outcomes of legal cases – such as Supreme Court decisions – using Artificial Intelligence (AI)? I recently had the opportunity to consider this point at a talk that I gave entitled "Machine Learning Within Law" at Stanford. The general idea behind such approaches is to use computer-based analysis of existing data (e.g. The approach to using data to inform legal predictions (as opposed to pure lawyerly analysis) has been largely championed by Prof. Katz – something that he has dubbed "Quantitative Legal Prediction" in recent work.