Law Commons^™

Open-Source Clinical Machine Learning Models: Critical Appraisal Of Feasibility, Advantages, And Challenges, Keerthi B. Harish, W. Nicholson Price Ii, Yindalon Aphinyanaphongs

Articles

Machine learning applications promise to augment clinical capabilities and at least 64 models have already been approved by the US Food and Drug Administration. These tools are developed, shared, and used in an environment in which regulations and market forces remain immature. An important consideration when evaluating this environment is the introduction of open-source solutions in which innovations are freely shared; such solutions have long been a facet of digital culture. We discuss the feasibility and implications of open-source machine learning in a health care infrastructure built upon proprietary information. The decreased cost of development as compared to drugs and …

New Innovation Models In Medical Ai, W Nicholson Price Ii, Rachel E. Sachs, Rebecca S. Eisenberg

Articles

In recent years, scientists and researchers have devoted considerable resources to developing medical artificial intelligence (AI) technologies. Many of these technologies—particularly those that resemble traditional medical devices in their functions—have received substantial attention in the legal and policy literature. But other types of novel AI technologies, such as those related to quality improvement and optimizing use of scarce facilities, have been largely absent from the discussion thus far. These AI innovations have the potential to shed light on important aspects of health innovation policy. First, these AI innovations interact less with the legal regimes that scholars traditionally conceive of as …

Terrified By Technology: How Systemic Bias Distorts U.S. Legal And Regulatory Responses To Emerging Technology, Steve Calandrillo, Nolan Kobuke Anderson

Articles

Americans are becoming increasingly aware of the systemic biases we possess and how those biases preclude us from collectively living out the true meaning of our national creed. But to fully understand systemic bias we must acknowledge that it is pervasive and extends beyond the contexts of race, privilege, and economic status. Understanding all forms of systemic bias helps us to better understand ourselves and our shortcomings. At first glance, a human bias against emerging technology caused by systemic risk misperception might seem uninteresting or unimportant. But this Article demonstrates how the presence of systemic bias anywhere, even in an …

Modeling Through, Ryan Calo

Articles

Theorists of justice have long imagined a decision-maker capable of acting wisely in every circumstance. Policymakers seldom live up to this ideal. They face well-understood limits, including an inability to anticipate the societal impacts of state intervention along a range of dimensions and values. Policymakers cannot see around corners or address societal problems at their roots. When it comes to regulation and policy-setting, policymakers are often forced, in the memorable words of political economist Charles Lindblom, to “muddle through” as best they can.

Powerful new affordances, from supercomputing to artificial intelligence, have arisen in the decades since Lindblom’s 1959 article …