Georgetown University Law Center

About: Georgetown University Law Center is a based out in . It is known for research contribution in the topics: Supreme court & Global health. The organization has 585 authors who have published 2488 publications receiving 36650 citations. The organization is also known as: Georgetown Law & GULC.

Public goods, private data: HIV and the history, ethics, and uses of identifiable public health information.

Abstract: b d Public health departments collect a vast array of identifiable information in the course of mandatory reporting efforts and other surveillance activities. These undertakings span a range of conditions from infectious threats and chronic diseases including cancer, to immunization status and birth defects. Advocates for expanded surveillance extended the practice to occupational diseases and, most ambitiously, to profiles of childhood health status. Syndromic surveillance is also increasingly undertaken in the new post-September 11 security environ - ment. In the one Supreme Court case addressing public health surveillance, a unanimous tribunal upheld the right of the state to conduct surveillance. 1

The Myth of the Law-Fact Distinction

Abstract: We consider and reject the idea that there is an ontological, epistemological, or analytical distinction between questions of law and questions of fact. Rather, we contend that the labels “law” and “fact” refer to the allocation of decision-making authority for pragmatic reasons. This allocation is driven by three factors: (1) the judge-jury relationship, (2) standard conventions concerning the meaning of “law” and “fact,” and (3) a distinction between matters of general import and highly specific and localized phenomena.

vGUPPI: Scoring Unilateral Pricing Incentives in Vertical Mergers

Abstract: One key concern in vertical merger cases is input foreclosure. Input foreclosure involves raising the costs of competitors in the downstream market, which could in turn increase the sales and profits of the downstream merger partner. In this article, we explain how the upward pricing pressure resulting from unilateral incentives following a vertical merger can be scored with vertical Gross Upward Pricing Pressure Indices (“vGUPPIs”). These vGUPPIs are derived from an economic model where upstream firms sell differentiated inputs to downstream firms which in turn sell differentiated products. There are separate vGUPPIs for the upstream and downstream merging firms and, in addition, vGUPPIs for the rivals of the downstream firm whose costs are raised. Our model also explains how the vGUPPIs can account for potential input substitution and merger-specific elimination of double marginalization. These vGUPPIs are analogous to the horizontal GUPPIs commonly used for the evaluation of unilateral effects in horizontal mergers. Like the horizontal GUPPIs, the vGUPPIs provide more direct evidence on unilateral pricing incentives than other metrics commonly carried out in vertical merger cases, such as concentration indices and vertical arithmetic. They also are simpler to implement and require less data than merger simulation models.The Appendix:The Appendix supplements the technical analysis in the Moresi and Salop Vertical GUPPI article published in the Antitrust Law Journal.The appendices for this paper are available at the following URL: http://ssrn.com/abstract=2368872

When Does Physician Use of AI Increase Liability

Abstract: Rationale: An increasing number of automated and artificially intelligent (AI) systems make medical treatment recommendations, including “personalized” recommendations, which can deviate from standard care. Legal scholars argue that following such nonstandard treatment recommendations will increase liability in medical malpractice, undermining the use of potentially beneficial medical AI. However, such liability depends in part on lay judgments by jurors: When physicians use AI systems, in which circumstances would jurors hold physicians liable? Methods: To determine potential jurors’ judgments of liability, we conducted an online experimental study of a nationally representative sample of 2,000 U.S. adults. Each participant read one of four scenarios in which an AI system provides a treatment recommendation to a physician. The scenarios varied the AI recommendation (standard or nonstandard care) and the physician’s decision (to accept or reject that recommendation). Subsequently, the physician’s decision caused a harm. Participants then assessed the physician’s liability. Results: Our results indicate that physicians who receive advice from an AI system to provide standard care can reduce the risk of liability by accepting, rather than rejecting, that advice, all else equal. However, when an AI system recommends nonstandard care, there is no similar shielding effect of rejecting that advice and so providing standard care. Conclusion: The tort law system is unlikely to undermine the use of AI precision medicine tools and may even encourage the use of these tools.