Torts

The latest development in class action litigation is the “future stakes settlement.” Under this novel mechanism, unveiled in the settlement proposal to end a privacy law class action lawsuit against the startup Clearview AI, a defendant grants a privately traded equity stake to the class in exchange for a release of all claims.

Future stakes settlements, though similar to existing mechanisms in class action and bankruptcy law, offer distinct benefits and costs. Through a future stakes settlement, the class may recover against a cashless defendant and receive a larger payout than would be possible through a traditional cash damages fund. But this recovery is uncertain, as the value of a future stake can fluctuate. Furthermore, by transforming injured parties into shareholders, future stakes settlements pose serious moral quandaries.

Existing guidance for settlement agreements under Federal Rule of Civil Procedure 23(e) is insufficient to handle the high degree of risk associated with future stakes settlements. This Comment recommends additional standards that courts should apply when evaluating these settlements. Through these additions, courts can prevent defendant gamesmanship, ensure future stakes settlements are fair to the class, and fulfill the dual purposes of compensation and regulation in class actions.

This Case Note first reviews the origins of the postal-matter exception and the FTCA. Then, it analyzes the Fifth Circuit’s holding in Konan and explores contrasting precedent in other circuits, most notably in the First and Second Circuits. Finally, this Note discusses the difficulty of balancing USPS’s interests against enabling suits under the FTCA and considers the implications of providing a tort remedy.

Causal AI is within reach. It has the potential to trigger nothing less than a conceptual revolution in the law. This Essay explains why and takes a cautious look into the crystal ball. Causation is an elusive concept in many disciplines—not only the law, but also science and statistics. Even the most up-to-date artificial intelligence systems do not “understand” causation, as they remain limited to the analysis of text and images. It is a long-standing statistical axiom that it is impossible to infer causation from the correlation of variables in datasets. This thwarts the extraction of causal relations from observational data. But important advances in computer science will enable us to distinguish between mere correlation and factual causation. At the same time, artificially intelligent systems are beginning to learn how to “think causally.”

After Ricardo Saldana suffered a stroke in 2014, his family moved him into Elms Convalescent Hospital, a skilled nursing facility in Glendale, California, so he could receive the care he needed.

Slices and Lumps is a recipe book for thinking. Using a deceptively simple analytical framework, the book showcases the power of conceptualizing the world through the prism of “slices” and “lumps.”