Across the post-Dobbs United States, reports of pregnant people battling infections as severe as sepsis, experiencing hemorrhaging, and suffering from other pregnancy complications in hospital emergency rooms are flooding the news. Because of state abortion bans’ lack of clarity about medical exceptions and the overall chilling effect on abortion care, many patients are being denied the emergency care that they need or are being forced to wait until they are knocking on death’s door before medical staff can treat them. Some patients are being airlifted to out-of-state hospitals for treatment elsewhere, and many who are forced to wait until their health deteriorates are taking matters into their own hands and choosing to travel out-of-state—often at great health risk—if they have the means.

Across the United States, immigrants held in for-profit detention centers participate, willingly or through degrees of coercion, in a work program that pays one dollar per day. For decades, the courts affirmed the legality of this practice and swiftly dismissed claims that participants in the program qualified for worker protections. But in the past decade, litigators, advocates, and academics have partnered with detained workers to successfully challenge the legality of these labor schemes, most recently scoring a unanimous victory at the Supreme Court.

For victims of abuse, safety means hiding. Not just hiding themselves, but also their contact details, their address, their workplace, their roommates, and any other information that could enable their abuser to target them. Yet today, no number of name changes and relocations can prevent data brokers from sharing a victim’s personal information online. Thanks to brokers, abusers can find what they need with a single search, a few clicks, and a few dollars. For many victims, then, the best hope for safety lies in obscurity—that is, making themselves and their information harder to find. This Article exposes privacy law’s complicity in this phenomenon of “brokered abuse.” Today, victims seeking obscurity can ask data brokers to remove their online information.

This Article is the first to scrutinize presidential trade authority under the Constitution. The Constitution grants the President no independent power to regulate foreign commerce. That conclusion, while apparent from a straightforward reading of Articles I and II, stands in stark contrast to executive conduct of U.S. trade policy in recent years. This Article traces the roots of this constitutional distortion to a confluence of doctrinal drift and academic oversight. Courts and commentators have increasingly relied on an expansive conception of executive power grounded in a perceived general foreign affairs authority. In doing so, they have blurred the line between diplomacy and commerce and used this confluence to justify unilateral economic actions by a “trader in chief” that circumvent the Constitution’s allocation of power.

This Article provides the first comprehensive account of the reconstruction of energy tax law that has occurred in the 2020s. In the past, federal energy policy offered carrots and sticks aimed selectively at specific sources of emissions (e.g., power plants) and specific green alternatives (e.g., solar and wind), even as academics urged the use of universal sticks like a carbon tax. But Congress has now charted a new path: performance-based carrots, or tax credits for any zero-emission energy technology (subject to certain politically driven exclusions). The only way to implement universal, performance-based carrots is to estimate the carbon intensity of every subsidy applicant. This is the task of carbon accounting. The Article makes two main arguments about the emergence of carbon accounting inside tax law.

To comply with the colorblind impulses of American antidiscrimination law, computer programmers tend to exclude race as a data input when constructing a machine learning algorithm. Yet scholars and advocates consistently argue that even these formally race-blind algorithms can racially discriminate by relying on so-called “proxies for race,” or variables that have a strong correlation with race, such as zip code, income, or prior criminal arrest. While a programmer wishing to respond to this argument might attempt to remove both race and all racial proxies from input data, their task is complicated by a key dilemma: The definition of a racial proxy is far from obvious. This Article examines the myriad definitions of a racial proxy proffered by courts, scholars, and state and private actors to demonstrate how race and racial assumptions become embedded in the machine learning algorithms that increasingly structure human life.