Jury Nullification in Modified Comparative Negligence Regimes

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.”

Professor Monica Haymond’s Intervention and Universal Remedies article invites scholars to focus on the distinctive ways that public law litigation plays out in practice. This Essay takes up her challenge. By questioning common assumptions at the core of structural-reform litigation, this Essay explains the dangers of consent decrees, settlements, and broad precedents. It then goes on to argue that intervention is an important check on these risks, and should be much more freely available in structural reform cases.

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.