Zach
Griffen

The abiding concern of my research is an updated version of a classic sociological question: is the purpose of quantitative knowledge to understand the world, or to change it? My current work is divided into three primary areas:

1. My book project, Expertise and the Enigma of Policy Influence, rethinks and reframes the enormously consequential economics of U.S. social policy as a predominantly reactive enterprise, in which existing social programs and data sources constrain economists' capacity to effect policy change. I find that when it comes to topics like healthcare or education, economics is not an unchanging monolith in policy settings, and that the further one gets from the field’s disciplinary core, economic theory is less essential to the work of economists than a common methodological language (which is not always legible to policy audiences). In the wake of the COVID-19 pandemic, this analytical approach has found economists joining the fray of experts investigating issues such as 'health equity,' a state of affairs which contrasts sharply with popular critiques of the field. Research related to this project has been published in Theory and Society, the Journal of Cultural Economy, Science, Technology, & Human Values, the Journal of Education Policy, and Economy and Society. Most recently, I wrote a piece for TIME Magazine's Made By History series based on this research.

2. My latest work channels my interest in quantification into the increasingly salient intersection between big data and healthcare. Along with colleagues at NYU, I am investigating how experts in the field of clinical informatics are conceptualizing governance of AI and machine learning in healthcare in the absence of an established regulatory framework. While some experts consider the FDA to be a model regulatory agency, others propose that a governance framework could be pieced together by drawing from a wider variety of systems in which safety and responsibility are key to technical innovation, including air traffic control, space travel, car service manuals, in vitro fertilization, self-driving cars, and X-ray technology. Early work based on this project is forthcoming in the American Journal of Bioethics.

3. A final project, Polygenic Prediction, turns a sociological lens on the production of new quantitative indicators in the field of behavior genetics that are beset by a host of uncertainties. While so-called polygenic scores have received critical attention primarily for their potentially eugenic implications in policy settings, this project investigates how uncertainty is inherent to research in this domain beginning with the collection of biobank data that disproportionately feature people with European ancestry, resulting in reference 'populations' that are not representative. My research on polygenic scores is a collaboration with UCLA's Aaron Panofsky and Nanibaa' Garrison that aims to empower clinicians, policymakers, and the public so that when polygenic prediction is applied, it occurs as ethically and equitably as possible.