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Full-Text Articles in Law
The Mismatched Goals Of Bankruptcy And Mass Tort Litigation, Maureen Carroll
The Mismatched Goals Of Bankruptcy And Mass Tort Litigation, Maureen Carroll
Reviews
By the end of this Term, SCOTUS must decide what to do about the mammoth Purdue Pharma bankruptcy settlement. If allowed to go forward, the $10 billion deal will not only resolve claims against the company, it will shield the Sackler family—the company’s former owners—from any further liability for their role in the opioid crisis. The deal has generated a great deal of discussion, much of it focused on the legality and wisdom of that third-party release. The authors of Against Bankruptcy take a broader view, asking a set of critical questions about the proper role of bankruptcy in the …
Destined To Deceive: The Need To Regulate Deepfakes With A Foreseeable Harm Standard, Matthew D. Weiner
Destined To Deceive: The Need To Regulate Deepfakes With A Foreseeable Harm Standard, Matthew D. Weiner
Michigan Law Review
Political campaigns have always attracted significant attention, and politicians have often been the subjects of controversial—even outlandish—discourse. In the last several years, however, the risk of deception has drastically increased due to the rise of “deepfakes.” Now, practically anyone can make audiovisual media that are both highly believable and highly damaging to a candidate. The threat deepfakes pose to our elections has prompted several states and Congress to seek legislative remedies that ensure recourse for victims and hold bad actors liable. These recent attempts at deepfake laws are open to attack from two different loci. First, there is a question …
Locating Liability For Medical Ai, W. Nicholson Price Ii, I. Glenn Cohen
Locating Liability For Medical Ai, W. Nicholson Price Ii, I. Glenn Cohen
Articles
When medical AI systems fail, who should be responsible, and how? We argue that various features of medical AI complicate the application of existing tort doctrines and render them ineffective at creating incentives for the safe and effective use of medical AI. In addition to complexity and opacity, the problem of contextual bias, where medical AI systems vary substantially in performance from place to place, hampers traditional doctrines. We suggest instead the application of enterprise liability to hospitals—making them broadly liable for negligent injuries occurring within the hospital system—with an important caveat: hospitals must have access to the information needed …